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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="keyword"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 78</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: keyword</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">78</span> The Impact of Keyword and Full Video Captioning on Listening Comprehension</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elias%20Bensalem">Elias Bensalem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates the effect of two types of captioning (full and keyword captioning) on listening comprehension. Thirty-six university-level EFL students participated in the study. They were randomly assigned to watch three video clips under three conditions. The first group watched the video clips with full captions. The second group watched the same video clips with keyword captions. The control group watched the video clips without captions. After watching each clip, participants took a listening comprehension test. At the end of the experiment, participants completed a questionnaire to measure their perceptions about the use of captions and the video clips they watched. Results indicated that the full captioning group significantly outperformed both the keyword captioning and the no captioning group on the listening comprehension tests. However, this study did not find any significant difference between the keyword captioning group and the no captioning group. Results of the survey suggest that keyword captioning were a source of distraction for participants. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=captions" title="captions">captions</a>, <a href="https://publications.waset.org/abstracts/search?q=EFL" title=" EFL"> EFL</a>, <a href="https://publications.waset.org/abstracts/search?q=listening%20comprehension" title=" listening comprehension"> listening comprehension</a>, <a href="https://publications.waset.org/abstracts/search?q=video" title=" video"> video</a> </p> <a href="https://publications.waset.org/abstracts/62467/the-impact-of-keyword-and-full-video-captioning-on-listening-comprehension" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62467.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">262</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">77</span> Empirical Study on Factors Influencing SEO</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pakinee%20Aimmanee">Pakinee Aimmanee</a>, <a href="https://publications.waset.org/abstracts/search?q=Phoom%20Chokratsamesiri"> Phoom Chokratsamesiri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Search engine has become an essential tool nowadays for people to search for their needed information on the internet. In this work, we evaluate the performance of the search engine from three factors: the keyword frequency, the number of inbound links, and the difficulty of the keyword. The evaluations are based on the ranking position and the number of days that Google has seen or detect the webpage. We find that the keyword frequency and the difficulty of the keyword do not affect the Google ranking where the number of inbound links gives remarkable improvement of the ranking position. The optimal number of inbound links found in the experiment is 10. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SEO" title="SEO">SEO</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20search" title=" web search"> web search</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20technologies" title=" knowledge technologies"> knowledge technologies</a> </p> <a href="https://publications.waset.org/abstracts/9414/empirical-study-on-factors-influencing-seo" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9414.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">283</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">76</span> Keyword Network Analysis on the Research Trends of Life-Long Education for People with Disabilities in Korea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jakyoung%20Kim">Jakyoung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungwook%20Jang"> Sungwook Jang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to examine the research trends of life-long education for people with disabilities using a keyword network analysis. For this purpose, 151 papers were selected from 594 papers retrieved using keywords such as 'people with disabilities' and 'life-long education' in the Korean Education and Research Information Service. The Keyword network analysis was constructed by extracting and coding the keyword used in the title of the selected papers. The frequency of the extracted keywords, the centrality of degree, and betweenness was analyzed by the keyword network. The results of the keyword network analysis are as follows. First, the main keywords that appeared frequently in the study of life-long education for people with disabilities were 'people with disabilities', 'life-long education', 'developmental disabilities', 'current situations', 'development'. The research trends of life-long education for people with disabilities are focused on the current status of the life-long education and the program development. Second, the keyword network analysis and visualization showed that the keywords with high frequency of occurrences also generally have high degree centrality and betweenness centrality. In terms of the keyword network diagram, it was confirmed that research trends of life-long education for people with disabilities are centered on six prominent keywords. Based on these results, it was discussed that life-long education for people with disabilities in the future needs to expand the subjects and the supporting areas of the life-long education, and the research needs to be further expanded into more detailed and specific areas.  <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=life-long%20education" title="life-long education">life-long education</a>, <a href="https://publications.waset.org/abstracts/search?q=people%20with%20disabilities" title=" people with disabilities"> people with disabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=research%20trends" title=" research trends"> research trends</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20network%20analysis" title=" keyword network analysis"> keyword network analysis</a> </p> <a href="https://publications.waset.org/abstracts/72646/keyword-network-analysis-on-the-research-trends-of-life-long-education-for-people-with-disabilities-in-korea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72646.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">75</span> Modified Active (MA) Algorithm to Generate Semantic Web Related Clustered Hierarchy for Keyword Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Leena%20Giri">G. Leena Giri</a>, <a href="https://publications.waset.org/abstracts/search?q=Archana%20Mathur"> Archana Mathur</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Manjula"> S. H. Manjula</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20R.%20Venugopal"> K. R. Venugopal</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20M.%20Patnaik"> L. M. Patnaik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Keyword search in XML documents is based on the notion of lowest common ancestors in the labelled trees model of XML documents and has recently gained a lot of research interest in the database community. In this paper, we propose the Modified Active (MA) algorithm which is an improvement over the active clustering algorithm by taking into consideration the entity aspect of the nodes to find the level of the node pertaining to a particular keyword input by the user. A portion of the bibliography database is used to experimentally evaluate the modified active algorithm and results show that it performs better than the active algorithm. Our modification improves the response time of the system and thereby increases the efficiency of the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword%20matching%20patterns" title="keyword matching patterns">keyword matching patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=MA%20algorithm" title=" MA algorithm"> MA algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20search" title=" semantic search"> semantic search</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a> </p> <a href="https://publications.waset.org/abstracts/6608/modified-active-ma-algorithm-to-generate-semantic-web-related-clustered-hierarchy-for-keyword-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6608.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">413</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">74</span> Optimization Query Image Using Search Relevance Re-Ranking Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20G.%20Asmitha%20Chandini">T. G. Asmitha Chandini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Web-based image search re-ranking, as an successful method to get better the results. In a query keyword, the first stair is store the images is first retrieve based on the text-based information. The user to select a query keywordimage, by using this query keyword other images are re-ranked based on their visual properties with images.Now a day to day, people projected to match images in a semantic space which is used attributes or reference classes closely related to the basis of semantic image. though, understanding a worldwide visual semantic space to demonstrate highly different images from the web is difficult and inefficient. The re-ranking images, which automatically offline part learns dissimilar semantic spaces for different query keywords. The features of images are projected into their related semantic spaces to get particular images. At the online stage, images are re-ranked by compare their semantic signatures obtained the semantic précised by the query keyword image. The query-specific semantic signatures extensively improve both the proper and efficiency of image re-ranking. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Query" title="Query">Query</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword" title=" keyword"> keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/abstracts/search?q=re-ranking" title=" re-ranking"> re-ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic" title=" semantic"> semantic</a>, <a href="https://publications.waset.org/abstracts/search?q=signature" title=" signature"> signature</a> </p> <a href="https://publications.waset.org/abstracts/28398/optimization-query-image-using-search-relevance-re-ranking-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28398.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">550</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">73</span> Effect of the Keyword Strategy on Lexical Semantic Acquisition: Recognition, Retention and Comprehension in an English as Second Language Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatima%20Muhammad%20Shitu">Fatima Muhammad Shitu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study seeks to investigate the effect of the keyword strategy on lexico–semantic acquisition, recognition, retention and comprehension in an ESL context. The aim of the study is to determine whether the keyword strategy can be used to enhance acquisition. As a quasi- experimental research, the objectives of the study include: To determine the extent to which the scores obtained by the subjects, who were trained on the use of the keyword strategy for acquisition, differ at the pre-tests and the post–tests and also to find out the relationship in the scores obtained at these tests levels. The sample for the study consists of 300 hundred undergraduate ESL Students in the Federal College of Education, Kano. The seventy-five lexical items for acquisition belong to the lexical field category known as register, and they include Medical, Agriculture and Photography registers (MAP). These were divided in the ratio twenty-five (25) lexical items in each lexical field. The testing technique was used to collect the data while the descriptive and inferential statistics were employed for data analysis. For the purpose of testing, the two kinds of tests administered at each test level include the WARRT (Word Acquisition, Recognition, and Retention Test) and the CCPT (Cloze Comprehension Passage Test). The results of the study revealed that there are significant differences in the scores obtained between the pre-tests, and the post–tests and there are no correlations in the scores obtained as well. This implies that the keyword strategy has effectively enhanced the acquisition of the lexical items studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword" title="keyword">keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=lexical" title=" lexical"> lexical</a>, <a href="https://publications.waset.org/abstracts/search?q=semantics" title=" semantics"> semantics</a>, <a href="https://publications.waset.org/abstracts/search?q=strategy" title=" strategy"> strategy</a> </p> <a href="https://publications.waset.org/abstracts/50708/effect-of-the-keyword-strategy-on-lexical-semantic-acquisition-recognition-retention-and-comprehension-in-an-english-as-second-language-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50708.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">311</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">72</span> Keyword Advertising: Still Need Construction in European Union; Perspective on Interflora vs. Marks and Spencer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammadbagher%20Asghariaghamashhadi">Mohammadbagher Asghariaghamashhadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Internet users normally are automatically linked to an advertisement sponsored by a bidder when Internet users enter any trademarked keyword on a search engine. This advertisement appears beside the search results. Through the process of keyword advertising, advertisers can connect with many Internet users and let them know about their goods and services. This concept has generated heated disagreements among legal scholars, trademark proprietors, advertisers, search engine owners, and consumers. Therefore, use of trademarks in keyword advertising has been one of the most debatable issues in trademark law for several years. This entirely new way of using trademarks over the Internet has provoked a discussion concerning the core concepts of trademark law. In respect to legal issues, European Union (EU) trademark law is mostly governed by the Trademark Directive and the Community Trademark Regulation. Article 5 of the directive and Article 9 of the trademark regulation determine the circumstances in which a trademark owner holds the right to prohibit a third party’s use of his/her registered sign. Harmonized EU trademark law proved to be ambiguous on whether using of a trademark is amounted to trademark infringement or not. The case law of the European Court of Justice (ECJ), with reference to this legislation, is mostly unfavorable to trademark owners. This ambivalence was also exhibited by the case law of EU Member States. European keyword advertisers simply could not tell which use of a competitor‘s trademark was lawful. In recent years, ECJ has continuously expanded the scope and reach of trademark protection in the EU. It is notable that Inconsistencies in the Court’s system of infringement criteria clearly come to the fore and this approach has been criticized by analysts who believe that the Court should have adopted a more traditional approach to the analysis of trademark infringement, which was suggested by its Advocate General, in order to arrive at the same conclusion. Regarding case law of keyword advertising within Europe, one of the most disputable cases is Interflora vs. Marks and Spencer, which is still on-going. This study examines and critically analyzes the decisions of the ECJ, the high court of England, and the Court of Appeals of England and address critically keyword advertising issue within European trademark legislation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECJ" title="ECJ">ECJ</a>, <a href="https://publications.waset.org/abstracts/search?q=Google" title=" Google"> Google</a>, <a href="https://publications.waset.org/abstracts/search?q=Interflora" title=" Interflora"> Interflora</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20advertising" title=" keyword advertising"> keyword advertising</a>, <a href="https://publications.waset.org/abstracts/search?q=Marks%20and%20Spencer" title=" Marks and Spencer"> Marks and Spencer</a>, <a href="https://publications.waset.org/abstracts/search?q=trademark%20infringement" title=" trademark infringement"> trademark infringement</a> </p> <a href="https://publications.waset.org/abstracts/51502/keyword-advertising-still-need-construction-in-european-union-perspective-on-interflora-vs-marks-and-spencer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51502.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">71</span> Graph-Based Semantical Extractive Text Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mina%20Samizadeh">Mina Samizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them), has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. This algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as a result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework, which can be used individually or as a part of generating the summary to overcome coverage problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword%20extraction" title="keyword extraction">keyword extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=n-gram%20extraction" title=" n-gram extraction"> n-gram extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20summarization" title=" text summarization"> text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20clustering" title=" topic clustering"> topic clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20analysis" title=" semantic analysis"> semantic analysis</a> </p> <a href="https://publications.waset.org/abstracts/160526/graph-based-semantical-extractive-text-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160526.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">70</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">70</span> An Open Source Advertisement System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pushkar%20Umaranikar">Pushkar Umaranikar</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20Pollett"> Chris Pollett</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An online advertisement system and its implementation for the Yioop open source search engine are presented. This system supports both selling advertisements and displaying them within search results. The selling of advertisements is done using a system to auction off daily impressions for keyword searches. This is an open, ascending price auction system in which all accepted bids will receive a fraction of the auctioned day’s impressions. New bids in our system are required to be at least one half of the sum of all previous bids ensuring the number of accepted bids is logarithmic in the total ad spend on a keyword for a day. The mechanics of creating an advertisement, attaching keywords to it, and adding it to an advertisement inventory are described. The algorithm used to go from accepted bids for a keyword to which ads are displayed at search time is also presented. We discuss properties of our system and compare it to existing auction systems and systems for selling online advertisements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20markets" title="online markets">online markets</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20ad%20system" title=" online ad system"> online ad system</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20auctions" title=" online auctions"> online auctions</a>, <a href="https://publications.waset.org/abstracts/search?q=search%20engines" title=" search engines"> search engines</a> </p> <a href="https://publications.waset.org/abstracts/46605/an-open-source-advertisement-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46605.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">326</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">69</span> Identification of Spam Keywords Using Hierarchical Category in C2C E-Commerce</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shao%20Bo%20Cheng">Shao Bo Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong-Jin%20Han"> Yong-Jin Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Se%20Young%20Park"> Se Young Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Seong-Bae%20Park"> Seong-Bae Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Consumer-to-Consumer (C2C) E-commerce has been growing at a very high speed in recent years. Since identical or nearly-same kinds of products compete one another by relying on keyword search in C2C E-commerce, some sellers describe their products with spam keywords that are popular but are not related to their products. Though such products get more chances to be retrieved and selected by consumers than those without spam keywords, the spam keywords mislead the consumers and waste their time. This problem has been reported in many commercial services like e-bay and taobao, but there have been little research to solve this problem. As a solution to this problem, this paper proposes a method to classify whether keywords of a product are spam or not. The proposed method assumes that a keyword for a given product is more reliable if the keyword is observed commonly in specifications of products which are the same or the same kind as the given product. This is because that a hierarchical category of a product in general determined precisely by a seller of the product and so is the specification of the product. Since higher layers of the hierarchical category represent more general kinds of products, a reliable degree is differently determined according to the layers. Hence, reliable degrees from different layers of a hierarchical category become features for keywords and they are used together with features only from specifications for classification of the keywords. Support Vector Machines are adopted as a basic classifier using the features, since it is powerful, and widely used in many classification tasks. In the experiments, the proposed method is evaluated with a golden standard dataset from Yi-han-wang, a Chinese C2C e-commerce, and is compared with a baseline method that does not consider the hierarchical category. The experimental results show that the proposed method outperforms the baseline in F1-measure, which proves that spam keywords are effectively identified by a hierarchical category in C2C e-commerce. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spam%20keyword" title="spam keyword">spam keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title=" e-commerce"> e-commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20features" title=" keyword features"> keyword features</a>, <a href="https://publications.waset.org/abstracts/search?q=spam%20%EF%AC%81ltering" title=" spam ﬁltering"> spam ﬁltering</a> </p> <a href="https://publications.waset.org/abstracts/15501/identification-of-spam-keywords-using-hierarchical-category-in-c2c-e-commerce" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15501.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">294</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">68</span> Sentence vs. Keyword Content Analysis in Intellectual Capital Disclosures Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Martin%20Surya%20Mulyadi">Martin Surya Mulyadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunita%20Anwar"> Yunita Anwar</a>, <a href="https://publications.waset.org/abstracts/search?q=Rosinta%20Ria%20Panggabean"> Rosinta Ria Panggabean</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Major transformations in economic activity from an agricultural economy to knowledge economy have led to an increasing focus on intellectual capital (IC) that has been characterized by continuous innovation, the spread of digital and communication technologies, intangible and human factors. IC is defined as the possession of knowledge and experience, professional knowledge and skill, proper relationships and technological capacities, which when applied will give organizations a competitive advantage. All of IC report/disclosure could be captured from the corporate annual report as it is a communication device that allows a corporation to connect with various external and internal stakeholders. This study was conducted using sentence-content analysis of IC disclosure in the annual report. This research aims to analyze whether the keyword-content analysis is reliable research methodology for IC disclosure related research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intellectual%20capital" title="intellectual capital">intellectual capital</a>, <a href="https://publications.waset.org/abstracts/search?q=intellectual%20capital%20disclosure" title=" intellectual capital disclosure"> intellectual capital disclosure</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20analysis" title=" content analysis"> content analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=annual%20report" title=" annual report"> annual report</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20analysis" title=" sentence analysis"> sentence analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20analysis" title=" keyword analysis"> keyword analysis</a> </p> <a href="https://publications.waset.org/abstracts/66892/sentence-vs-keyword-content-analysis-in-intellectual-capital-disclosures-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66892.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">67</span> Interactive, Topic-Oriented Search Support by a Centroid-Based Text Categorisation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mario%20Kubek">Mario Kubek</a>, <a href="https://publications.waset.org/abstracts/search?q=Herwig%20Unger"> Herwig Unger</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Centroid terms are single words that semantically and topically characterise text documents and so may serve as their very compact representation in automatic text processing. In the present paper, centroids are used to measure the relevance of text documents with respect to a given search query. Thus, a new graphbased paradigm for searching texts in large corpora is proposed and evaluated against keyword-based methods. The first, promising experimental results demonstrate the usefulness of the centroid-based search procedure. It is shown that especially the routing of search queries in interactive and decentralised search systems can be greatly improved by applying this approach. A detailed discussion on further fields of its application completes this contribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=search%20algorithm" title="search algorithm">search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=centroid" title=" centroid"> centroid</a>, <a href="https://publications.waset.org/abstracts/search?q=query" title=" query"> query</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword" title=" keyword"> keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=co-occurrence" title=" co-occurrence"> co-occurrence</a>, <a href="https://publications.waset.org/abstracts/search?q=categorisation" title=" categorisation"> categorisation</a> </p> <a href="https://publications.waset.org/abstracts/82581/interactive-topic-oriented-search-support-by-a-centroid-based-text-categorisation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82581.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">282</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">66</span> Analyzing Keyword Networks for the Identification of Correlated Research Topics </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thiago%20M.%20R.%20Dias">Thiago M. R. Dias</a>, <a href="https://publications.waset.org/abstracts/search?q=Patr%C3%ADcia%20M.%20Dias"> Patrícia M. Dias</a>, <a href="https://publications.waset.org/abstracts/search?q=Gray%20F.%20Moita"> Gray F. Moita</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The production and publication of scientific works have increased significantly in the last years, being the Internet the main factor of access and distribution of these works. Faced with this, there is a growing interest in understanding how scientific research has evolved, in order to explore this knowledge to encourage research groups to become more productive. Therefore, the objective of this work is to explore repositories containing data from scientific publications and to characterize keyword networks of these publications, in order to identify the most relevant keywords, and to highlight those that have the greatest impact on the network. To do this, each article in the study repository has its keywords extracted and in this way the network is  characterized, after which several metrics for social network analysis are applied for the identification of the highlighted keywords. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bibliometrics" title="bibliometrics">bibliometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction%20and%20data%20integration" title=" extraction and data integration"> extraction and data integration</a>, <a href="https://publications.waset.org/abstracts/search?q=scientometrics" title=" scientometrics"> scientometrics</a> </p> <a href="https://publications.waset.org/abstracts/67802/analyzing-keyword-networks-for-the-identification-of-correlated-research-topics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67802.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">257</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">65</span> Finding Related Scientific Documents Using Formal Concept Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nadeem%20Akhtar">Nadeem Akhtar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hira%20Javed"> Hira Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An important aspect of research is literature survey. Availability of a large amount of literature across different domains triggers the need for optimized systems which provide relevant literature to researchers. We propose a search system based on keywords for text documents. This experimental approach provides a hierarchical structure to the document corpus. The documents are labelled with keywords using KEA (Keyword Extraction Algorithm) and are automatically organized in a lattice structure using Formal Concept Analysis (FCA). This groups the semantically related documents together. The hierarchical structure, based on keywords gives out only those documents which precisely contain them. This approach open doors for multi-domain research. The documents across multiple domains which are indexed by similar keywords are grouped together. A hierarchical relationship between keywords is obtained. To signify the effectiveness of the approach, we have carried out the experiment and evaluation on Semeval-2010 Dataset. Results depict that the presented method is considerably successful in indexing of scientific papers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=formal%20concept%20analysis" title="formal concept analysis">formal concept analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20extraction%20algorithm" title=" keyword extraction algorithm"> keyword extraction algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=scientific%20documents" title=" scientific documents"> scientific documents</a>, <a href="https://publications.waset.org/abstracts/search?q=lattice" title=" lattice"> lattice</a> </p> <a href="https://publications.waset.org/abstracts/71401/finding-related-scientific-documents-using-formal-concept-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71401.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">64</span> Evidence of a Negativity Bias in the Keywords of Scientific Papers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kseniia%20Zviagintseva">Kseniia Zviagintseva</a>, <a href="https://publications.waset.org/abstracts/search?q=Brett%20Buttliere"> Brett Buttliere</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Science is fundamentally a problem-solving enterprise, and scientists pay more attention to the negative things, that cause them dissonance and negative affective state of uncertainty or contradiction. While this is agreed upon by philosophers of science, there are few empirical demonstrations. Here we examine the keywords from those papers published by PLoS in 2014 and show with several sentiment analyzers that negative keywords are studied more than positive keywords. Our dataset is the 927,406 keywords of 32,870 scientific articles in all fields published in 2014 by the journal PLOS ONE (collected from Altmetric.com). Counting how often the 47,415 unique keywords are used, we can examine whether those negative topics are studied more than positive. In order to find the sentiment of the keywords, we utilized two sentiment analysis tools, Hu and Liu (2004) and SentiStrength (2014). The results below are for Hu and Liu as these are the less convincing results. The average keyword was utilized 19.56 times, with half of the keywords being utilized only 1 time and the maximum number of uses being 18,589 times. The keywords identified as negative were utilized 37.39 times, on average, with the positive keywords being utilized 14.72 times and the neutral keywords - 19.29, on average. This difference is only marginally significant, with an F value of 2.82, with a p of .05, but one must keep in mind that more than half of the keywords are utilized only 1 time, artificially increasing the variance and driving the effect size down. To examine more closely, we looked at those top 25 most utilized keywords that have a sentiment. Among the top 25, there are only two positive words, ‘care’ and ‘dynamics’, in position numbers 5 and 13 respectively, with all the rest being identified as negative. ‘Diseases’ is the most studied keyword with 8,790 uses, with ‘cancer’ and ‘infectious’ being the second and fourth most utilized sentiment-laden keywords. The sentiment analysis is not perfect though, as the words ‘diseases’ and ‘disease’ are split by taking 1st and 3rd positions. Combining them, they remain as the most common sentiment-laden keyword, being utilized 13,236 times. More than just splitting the words, the sentiment analyzer logs ‘regression’ and ‘rat’ as negative, and these should probably be considered false positives. Despite these potential problems, the effect is apparent, as even the positive keywords like ‘care’ could or should be considered negative, since this word is most commonly utilized as a part of ‘health care’, ‘critical care’ or ‘quality of care’ and generally associated with how to improve it. All in all, the results suggest that negative concepts are studied more, also providing support for the notion that science is most generally a problem-solving enterprise. The results also provide evidence that negativity and contradiction are related to greater productivity and positive outcomes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bibliometrics" title="bibliometrics">bibliometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=keywords%20analysis" title=" keywords analysis"> keywords analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=negativity%20bias" title=" negativity bias"> negativity bias</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20and%20negative%20words" title=" positive and negative words"> positive and negative words</a>, <a href="https://publications.waset.org/abstracts/search?q=scientific%20papers" title=" scientific papers"> scientific papers</a>, <a href="https://publications.waset.org/abstracts/search?q=scientometrics" title=" scientometrics"> scientometrics</a> </p> <a href="https://publications.waset.org/abstracts/76958/evidence-of-a-negativity-bias-in-the-keywords-of-scientific-papers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76958.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">63</span> Frontier Dynamic Tracking in the Field of Urban Plant and Habitat Research: Data Visualization and Analysis Based on Journal Literature</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shao%20Qi">Shao Qi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article uses the CiteSpace knowledge graph analysis tool to sort and visualize the journal literature on urban plants and habitats in the Web of Science and China National Knowledge Infrastructure databases. Based on a comprehensive interpretation of the visualization results of various data sources and the description of the intrinsic relationship between high-frequency keywords using knowledge mapping, the research hotspots, processes and evolution trends in this field are analyzed. Relevant case studies are also conducted for the hotspot contents to explore the means of landscape intervention and synthesize the understanding of research theories. The results show that (1) from 1999 to 2022, the research direction of urban plants and habitats gradually changed from focusing on plant and animal extinction and biological invasion to the field of human urban habitat creation, ecological restoration, and ecosystem services. (2) The results of keyword emergence and keyword growth trend analysis show that habitat creation research has shown a rapid and stable growth trend since 2017, and ecological restoration has gained long-term sustained attention since 2004. The hotspots of future research on urban plants and habitats in China may focus on habitat creation and ecological restoration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=research%20trends" title="research trends">research trends</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20analysis" title=" visual analysis"> visual analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=habitat%20creation" title=" habitat creation"> habitat creation</a>, <a href="https://publications.waset.org/abstracts/search?q=ecological%20restoration" title=" ecological restoration"> ecological restoration</a> </p> <a href="https://publications.waset.org/abstracts/173295/frontier-dynamic-tracking-in-the-field-of-urban-plant-and-habitat-research-data-visualization-and-analysis-based-on-journal-literature" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173295.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">61</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">62</span> Methodologies for Deriving Semantic Technical Information Using an Unstructured Patent Text Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaehyung%20An">Jaehyung An</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungjoo%20Lee"> Sungjoo Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Patent documents constitute an up-to-date and reliable source of knowledge for reflecting technological advance, so patent analysis has been widely used for identification of technological trends and formulation of technology strategies. But, identifying technological information from patent data entails some limitations such as, high cost, complexity, and inconsistency because it rely on the expert’ knowledge. To overcome these limitations, researchers have applied to a quantitative analysis based on the keyword technique. By using this method, you can include a technological implication, particularly patent documents, or extract a keyword that indicates the important contents. However, it only uses the simple-counting method by keyword frequency, so it cannot take into account the sematic relationship with the keywords and sematic information such as, how the technologies are used in their technology area and how the technologies affect the other technologies. To automatically analyze unstructured technological information in patents to extract the semantic information, it should be transformed into an abstracted form that includes the technological key concepts. Specific sentence structure ‘SAO’ (subject, action, object) is newly emerged by representing ‘key concepts’ and can be extracted by NLP (Natural language processor). An SAO structure can be organized in a problem-solution format if the action-object (AO) states that the problem and subject (S) form the solution. In this paper, we propose the new methodology that can extract the SAO structure through technical elements extracting rules. Although sentence structures in the patents text have a unique format, prior studies have depended on general NLP (Natural language processor) applied to the common documents such as newspaper, research paper, and twitter mentions, so it cannot take into account the specific sentence structure types of the patent documents. To overcome this limitation, we identified a unique form of the patent sentences and defined the SAO structures in the patents text data. There are four types of technical elements that consist of technology adoption purpose, application area, tool for technology, and technical components. These four types of sentence structures from patents have their own specific word structure by location or sequence of the part of speech at each sentence. Finally, we developed algorithms for extracting SAOs and this result offer insight for the technology innovation process by providing different perspectives of technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NLP" title="NLP">NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=patent%20analysis" title=" patent analysis"> patent analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=SAO" title=" SAO"> SAO</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic-analysis" title=" semantic-analysis"> semantic-analysis</a> </p> <a href="https://publications.waset.org/abstracts/43480/methodologies-for-deriving-semantic-technical-information-using-an-unstructured-patent-text-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43480.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">262</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">61</span> A Corpus-Based Analysis of "MeToo" Discourse in South Korea: Coverage Representation in Korean Newspapers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sun-Hee%20Lee">Sun-Hee Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Amanda%20Kraley"> Amanda Kraley</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The “MeToo” movement is a social movement against sexual abuse and harassment. Though the hashtag went viral in 2017 following different cultural flashpoints in different countries, the initial response was quiet in South Korea. This radically changed in January 2018, when a high-ranking senior prosecutor, Seo Ji-hyun, gave a televised interview discussing being sexually assaulted by a colleague. Acknowledging public anger, particularly among women, on the long-existing problems of sexual harassment and abuse, the South Korean media have focused on several high-profile cases. Analyzing the media representation of these cases is a window into the evolving South Korean discourse around “MeToo.” This study presents a linguistic analysis of “MeToo” discourse in South Korea by utilizing a corpus-based approach. The term corpus (pl. corpora) is used to refer to electronic language data, that is, any collection of recorded instances of spoken or written language. A “MeToo” corpus has been collected by extracting newspaper articles containing the keyword “MeToo” from BIGKinds, big data analysis, and service and Nexis Uni, an online academic database search engine, to conduct this language analysis. The corpus analysis explores how Korean media represent accusers and the accused, victims and perpetrators. The extracted data includes 5,885 articles from four broadsheet newspapers (Chosun, JoongAng, Hangyore, and Kyunghyang) and 88 articles from two Korea-based English newspapers (Korea Times and Korea Herald) between January 2017 and November 2020. The information includes basic data analysis with respect to keyword frequency and network analysis and adds refined examinations of select corpus samples through naming strategies, semantic relations, and pragmatic properties. Along with the exponential increase of the number of articles containing the keyword “MeToo” from 104 articles in 2017 to 3,546 articles in 2018, the network and keyword analysis highlights ‘US,’ ‘Harvey Weinstein’, and ‘Hollywood,’ as keywords for 2017, with articles in 2018 highlighting ‘Seo Ji-Hyun, ‘politics,’ ‘President Moon,’ ‘An Ui-Jeong, ‘Lee Yoon-taek’ (the names of perpetrators), and ‘(Korean) society.’ This outcome demonstrates the shift of media focus from international affairs to domestic cases. Another crucial finding is that word ‘defamation’ is widely distributed in the “MeToo” corpus. This relates to the South Korean legal system, in which a person who defames another by publicly alleging information detrimental to their reputation—factual or fabricated—is punishable by law (Article 307 of the Criminal Act of Korea). If the defamation occurs on the internet, it is subject to aggravated punishment under the Act on Promotion of Information and Communications Network Utilization and Information Protection. These laws, in particular, have been used against accusers who have publicly come forward in the wake of “MeToo” in South Korea, adding an extra dimension of risk. This corpus analysis of “MeToo” newspaper articles contributes to the analysis of the media representation of the “MeToo” movement and sheds light on the shifting landscape of gender relations in the public sphere in South Korea. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corpus%20linguistics" title="corpus linguistics">corpus linguistics</a>, <a href="https://publications.waset.org/abstracts/search?q=MeToo" title=" MeToo"> MeToo</a>, <a href="https://publications.waset.org/abstracts/search?q=newspapers" title=" newspapers"> newspapers</a>, <a href="https://publications.waset.org/abstracts/search?q=South%20Korea" title=" South Korea"> South Korea</a> </p> <a href="https://publications.waset.org/abstracts/133017/a-corpus-based-analysis-of-metoo-discourse-in-south-korea-coverage-representation-in-korean-newspapers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">223</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">60</span> An Approach towards Intelligent Urbanism in New Communities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sherine%20Shafik%20Aly">Sherine Shafik Aly</a>, <a href="https://publications.waset.org/abstracts/search?q=Farida%20Ahmed%20El%20Mallah"> Farida Ahmed El Mallah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technology is a quoted keyword nowadays in all fields; it has been recently thought of and integrated into urban development. This research explains the role of technology in establishing intelligent urbanism to create a convivial and sustainable environment for people to live in. Cities are downgrading socially, economically and environmentally. A framework is to be developed where these three pillars are involved in the planning, design, and spreading of technology to create convivial environments. The aim of this research is achieved by highlighting the importance and approaches of intelligent urbanism, it’s characteristics and principles, then analyzing some relevant examples to achieve a set of guidelines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convivial" title="convivial">convivial</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent" title=" intelligent"> intelligent</a>, <a href="https://publications.waset.org/abstracts/search?q=technology" title=" technology"> technology</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20development" title=" urban development"> urban development</a> </p> <a href="https://publications.waset.org/abstracts/79697/an-approach-towards-intelligent-urbanism-in-new-communities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79697.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">260</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">59</span> A Review of Existing Turnover Intention Theories</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pauline%20E.%20Ngo-Henha">Pauline E. Ngo-Henha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Existing turnover intention theories are reviewed in this paper. This review was conducted with the help of the search keyword “turnover intention theories” in Google Scholar during the month of July 2017. These theories include: The Theory of Organizational Equilibrium (TOE), Social Exchange Theory, Job Embeddedness Theory, Herzberg’s Two-Factor Theory, the Resource-Based View, Equity Theory, Human Capital Theory, and the Expectancy Theory. One of the limitations of this review paper is that data were only collected from Google Scholar where many papers were sometimes not freely accessible. However, this paper attempts to contribute to the research in clarifying the distinction between theories and models in the context of turnover intention. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Literature%20Review" title="Literature Review">Literature Review</a>, <a href="https://publications.waset.org/abstracts/search?q=Theory" title=" Theory"> Theory</a>, <a href="https://publications.waset.org/abstracts/search?q=Turnover" title=" Turnover"> Turnover</a>, <a href="https://publications.waset.org/abstracts/search?q=Turnover%20intention" title=" Turnover intention"> Turnover intention</a> </p> <a href="https://publications.waset.org/abstracts/81252/a-review-of-existing-turnover-intention-theories" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81252.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">455</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">58</span> Nearest Neighbor Investigate Using R+ Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rutuja%20Desai">Rutuja Desai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Search engine is fundamentally a framework used to search the data which is pertinent to the client via WWW. Looking close-by spot identified with the keywords is an imperative concept in developing web advances. For such kind of searching, extent pursuit or closest neighbor is utilized. In range search the forecast is made whether the objects meet to query object. Nearest neighbor is the forecast of the focuses close to the query set by the client. Here, the nearest neighbor methodology is utilized where Data recovery R+ tree is utilized rather than IR2 tree. The disadvantages of IR2 tree is: The false hit number can surpass the limit and the mark in Information Retrieval R-tree must have Voice over IP bit for each one of a kind word in W set is recouped by Data recovery R+ tree. The inquiry is fundamentally subordinate upon the key words and the geometric directions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title="information retrieval">information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbor%20search" title=" nearest neighbor search"> nearest neighbor search</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20search" title=" keyword search"> keyword search</a>, <a href="https://publications.waset.org/abstracts/search?q=R%2B%20tree" title=" R+ tree"> R+ tree</a> </p> <a href="https://publications.waset.org/abstracts/33680/nearest-neighbor-investigate-using-r-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33680.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">289</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">57</span> Lecture Video Indexing and Retrieval Using Topic Keywords</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20J.%20Sandesh">B. J. Sandesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Saurabha%20Jirgi"> Saurabha Jirgi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Vidya"> S. Vidya</a>, <a href="https://publications.waset.org/abstracts/search?q=Prakash%20Eljer"> Prakash Eljer</a>, <a href="https://publications.waset.org/abstracts/search?q=Gowri%20Srinivasa"> Gowri Srinivasa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a framework to help users to search and retrieve the portions in the lecture video of their interest. This is achieved by temporally segmenting and indexing the lecture video using the topic keywords. We use transcribed text from the video and documents relevant to the video topic extracted from the web for this purpose. The keywords for indexing are found by applying the non-negative matrix factorization (NMF) topic modeling techniques on the web documents. Our proposed technique first creates indices on the transcribed documents using the topic keywords, and these are mapped to the video to find the start and end time of the portions of the video for a particular topic. This time information is stored in the index table along with the topic keyword which is used to retrieve the specific portions of the video for the query provided by the users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20indexing%20and%20retrieval" title="video indexing and retrieval">video indexing and retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=lecture%20videos" title=" lecture videos"> lecture videos</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20based%20video%20search" title=" content based video search"> content based video search</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20indexing" title=" multimodal indexing"> multimodal indexing</a> </p> <a href="https://publications.waset.org/abstracts/77066/lecture-video-indexing-and-retrieval-using-topic-keywords" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77066.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">250</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">56</span> Quantitative, Preservative Methodology for Review of Interview Transcripts Using Natural Language Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rowan%20P.%20Martnishn">Rowan P. Martnishn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> During the execution of a National Endowment of the Arts grant, approximately 55 interviews were collected from professionals across various fields. These interviews were used to create deliverables – historical connections for creations that began as art and evolved entirely into computing technology. With dozens of hours’ worth of transcripts to be analyzed by qualitative coders, a quantitative methodology was created to sift through the documents. The initial step was to both clean and format all the data. First, a basic spelling and grammar check was applied, as well as a Python script for normalized formatting which used an open-source grammatical formatter to make the data as coherent as possible. 10 documents were randomly selected to manually review, where words often incorrectly translated during the transcription were recorded and replaced throughout all other documents. Then, to remove all banter and side comments, the transcripts were spliced into paragraphs (separated by change in speaker) and all paragraphs with less than 300 characters were removed. Secondly, a keyword extractor, a form of natural language processing where significant words in a document are selected, was run on each paragraph for all interviews. Every proper noun was put into a data structure corresponding to that respective interview. From there, a Bidirectional and Auto-Regressive Transformer (B.A.R.T.) summary model was then applied to each paragraph that included any of the proper nouns selected from the interview. At this stage the information to review had been sent from about 60 hours’ worth of data to 20. The data was further processed through light, manual observation – any summaries which proved to fit the criteria of the proposed deliverable were selected, as well their locations within the document. This narrowed that data down to about 5 hours’ worth of processing. The qualitative researchers were then able to find 8 more connections in addition to our previous 4, exceeding our minimum quota of 3 to satisfy the grant. Major findings of the study and subsequent curation of this methodology raised a conceptual finding crucial to working with qualitative data of this magnitude. In the use of artificial intelligence there is a general trade off in a model between breadth of knowledge and specificity. If the model has too much knowledge, the user risks leaving out important data (too general). If the tool is too specific, it has not seen enough data to be useful. Thus, this methodology proposes a solution to this tradeoff. The data is never altered outside of grammatical and spelling checks. Instead, the important information is marked, creating an indicator of where the significant data is without compromising the purity of it. Secondly, the data is chunked into smaller paragraphs, giving specificity, and then cross-referenced with the keywords (allowing generalization over the whole document). This way, no data is harmed, and qualitative experts can go over the raw data instead of using highly manipulated results. Given the success in deliverable creation as well as the circumvention of this tradeoff, this methodology should stand as a model for synthesizing qualitative data while maintaining its original form. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.A.R.T.model" title="B.A.R.T.model">B.A.R.T.model</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20extractor" title=" keyword extractor"> keyword extractor</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=qualitative%20coding" title=" qualitative coding"> qualitative coding</a> </p> <a href="https://publications.waset.org/abstracts/189003/quantitative-preservative-methodology-for-review-of-interview-transcripts-using-natural-language-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189003.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">28</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">55</span> Composite Kernels for Public Emotion Recognition from Twitter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chien-Hung%20Chen">Chien-Hung Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan-Chun%20Hsing"> Yan-Chun Hsing</a>, <a href="https://publications.waset.org/abstracts/search?q=Yung-Chun%20Chang"> Yung-Chun Chang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition" title="emotion recognition">emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=composite%20kernel" title=" composite kernel"> composite kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a> </p> <a href="https://publications.waset.org/abstracts/97115/composite-kernels-for-public-emotion-recognition-from-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97115.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">218</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">54</span> Measuring Text-Based Semantics Relatedness Using WordNet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Madiha%20Khan">Madiha Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidrah%20Ramzan"> Sidrah Ramzan</a>, <a href="https://publications.waset.org/abstracts/search?q=Seemab%20Khan"> Seemab Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahzad%20Hassan"> Shahzad Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamran%20Saeed"> Kamran Saeed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Measuring semantic similarity between texts is calculating semantic relatedness between texts using various techniques. Our web application (Measuring Relatedness of Concepts-MRC) allows user to input two text corpuses and get semantic similarity percentage between both using WordNet. Our application goes through five stages for the computation of semantic relatedness. Those stages are: Preprocessing (extracts keywords from content), Feature Extraction (classification of words into Parts-of-Speech), Synonyms Extraction (retrieves synonyms against each keyword), Measuring Similarity (using keywords and synonyms, similarity is measured) and Visualization (graphical representation of similarity measure). Hence the user can measure similarity on basis of features as well. The end result is a percentage score and the word(s) which form the basis of similarity between both texts with use of different tools on same platform. In future work we look forward for a Web as a live corpus application that provides a simpler and user friendly tool to compare documents and extract useful information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Graphviz%20representation" title="Graphviz representation">Graphviz representation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20relatedness" title=" semantic relatedness"> semantic relatedness</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measurement" title=" similarity measurement"> similarity measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=WordNet%20similarity" title=" WordNet similarity"> WordNet similarity</a> </p> <a href="https://publications.waset.org/abstracts/95106/measuring-text-based-semantics-relatedness-using-wordnet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95106.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">237</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">53</span> Sentiment Analysis in Social Networks Sites Based on a Bibliometrics Analysis: A Comprehensive Analysis and Trends for Future Research Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jehan%20Fahim%20M.%20Alsulami">Jehan Fahim M. Alsulami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Academic research about sentiment analysis in sentiment analysis has obtained significant advancement over recent years and is flourishing from the collection of knowledge provided by various academic disciplines. In the current study, the status and development trend of the field of sentiment analysis in social networks is evaluated through a bibliometric analysis of academic publications. In particular, the distributions of publications and citations, the distribution of subject, predominant journals, authors, countries are analyzed. The collaboration degree is applied to measure scientific connections from different aspects. Moreover, the keyword co-occurrence analysis is used to find out the major research topics and their evolutions throughout the time span. The area of sentiment analysis in social networks has gained growing attention in academia, with computer science and engineering as the top main research subjects. China and the USA provide the most to the area development. Authors prefer to collaborate more with those within the same nation. Among the research topics, newly risen topics such as COVID-19, customer satisfaction are discovered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bibliometric%20analysis" title="bibliometric analysis">bibliometric analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20networks" title=" social networks"> social networks</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a> </p> <a href="https://publications.waset.org/abstracts/137597/sentiment-analysis-in-social-networks-sites-based-on-a-bibliometrics-analysis-a-comprehensive-analysis-and-trends-for-future-research-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137597.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">218</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">52</span> The Construction of Malaysian Airline Tragedies in Malaysian and British Online News: A Multidisciplinary Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Theng%20Theng%20Ong">Theng Theng Ong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study adopts a multidisciplinary method by combining the corpus-based discourse analysis study and language attitude study to explore the construction of Malaysia airline tragedies: MH370, MH17 and QZ8501 in the selected Malaysian and United Kingdom (UK) online news. The study aims to determine the ways in which Malaysian Airline tragedies MH370, MH17 and QZ8501 are linguistically defined and constructed in terms of keyword and collocation. The study also seeks to identify the types of discourse that are presented in the new articles. The differences or similarities in terms of keywords, topics or issues covered by the selected Malaysian and UK news media will also be examined. Finally, the language attitude study will be carried out to examine the Malaysia and UK university students’ attitudes toward the keywords, topics or issues covered by the selected Malaysian and UK news media pertaining to Malaysian Airline tragedies MH370, MH17 and QZ8501. The analysis is divided into two parts with the first part focusing on corpus-based discourse analysis on the media text. The second part of the study is to investigate Malaysians and UK news readers’ attitudes towards the online news being reported by the Malaysian and UK news media pertaining to the Airline tragedies. The main findings of corpus-based discourse analysis are essential in designing the questions in the questionnaires and interview and therefore led to the identification of the attitudes among Malaysian and UK news. This study adopts a multidisciplinary method by combining the corpus-based discourse analysis study and language attitude study to explore the construction of Malaysia airline tragedies: MH370, MH17 and QZ8501 in the selected Malaysian and United Kingdom (UK) online news. The study aims to determine the ways in which Malaysian Airline tragedies MH370, MH17 and QZ8501 are linguistically defined and constructed in terms of keyword and collocation. The study also seeks to identify the types of discourse that are presented in the new articles. The differences or similarities in terms of keywords, topics or issues covered by the selected Malaysian and UK news media will also be examined. Finally, the language attitude study will be carried out to examine the Malaysia and UK university students’ attitudes toward the keywords, topics or issues covered by the selected Malaysian and UK news media pertaining to Malaysian Airline tragedies MH370, MH17 and QZ8501. The analysis is divided into two parts with the first part focusing on corpus-based discourse analysis on the media text. The second part of the study is to investigate Malaysians and UK news readers’ attitudes towards the online news being reported by the Malaysian and UK news media pertaining to the Airline tragedies. The main findings of corpus-based discourse analysis are essential in designing the questions in the questionnaires and interview and therefore led to the identification of the attitudes among Malaysian and UK news. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corpus%20linguistics" title="corpus linguistics">corpus linguistics</a>, <a href="https://publications.waset.org/abstracts/search?q=critical%20discourse%20analysis" title=" critical discourse analysis"> critical discourse analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=news%20media" title=" news media"> news media</a>, <a href="https://publications.waset.org/abstracts/search?q=tragedies%20study" title=" tragedies study"> tragedies study</a> </p> <a href="https://publications.waset.org/abstracts/36363/the-construction-of-malaysian-airline-tragedies-in-malaysian-and-british-online-news-a-multidisciplinary-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36363.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">335</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">51</span> Numerical Modeling Analysis for the Double-Layered Asphalt Pavement Structure Behavior with Interface Bonding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Minh%20Tu%20Le">Minh Tu Le</a>, <a href="https://publications.waset.org/abstracts/search?q=Quang%20Huy%20Nguyen"> Quang Huy Nguyen</a>, <a href="https://publications.waset.org/abstracts/search?q=Mai%20Lan%20Nguyen"> Mai Lan Nguyen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bonding characteristics between pavement layers have an important influence on responses of pavement structures. This paper deals with analytical solution for the stresses, strains, and deflections of double-layered asphalt pavement structure. This solution is based on the homogeneous half-space of layered theory developed by Burmister (1943). The partial interaction between the layers is taken into account by considering an interface bonding behavior which is obtained by push-out shear test. Numerical applications considering three cases of bonding (unbonded, partially bonded, and fully bonded overlays) are carried out to the influence of the interface bonding on the structural behavior of asphalt pavement under static loading. Further, it was observed that numerical results indicate that the horizontal shear reaction modulus at the interface (Ks) will significantly affect pavement structure behavior. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytical%20solution" title="analytical solution">analytical solution</a>, <a href="https://publications.waset.org/abstracts/search?q=interface%20bonding" title=" interface bonding"> interface bonding</a>, <a href="https://publications.waset.org/abstracts/search?q=shear%20test%20keyword" title=" shear test keyword"> shear test keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=double-layered%20asphalt" title=" double-layered asphalt"> double-layered asphalt</a>, <a href="https://publications.waset.org/abstracts/search?q=shear%20reaction%20modulus" title=" shear reaction modulus"> shear reaction modulus</a> </p> <a href="https://publications.waset.org/abstracts/83012/numerical-modeling-analysis-for-the-double-layered-asphalt-pavement-structure-behavior-with-interface-bonding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83012.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">230</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">50</span> Climate Change and Tourism: A Scientometric Analysis Using Citespace</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yan%20Fang">Yan Fang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jie%20Yin"> Jie Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Bihu%20Wu"> Bihu Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The interaction between climate change and tourism is one of the most promising research areas of recent decades. In this paper, a scientometric analysis of 976 academic publications between 1990 and 2015 related to climate change and tourism is presented in order to characterize the intellectual landscape by identifying and visualizing the evolution of the collaboration network, the co-citation network, and emerging trends of citation burst and keyword co-occurrence. The results show that the number of publications in this field has increased rapidly and it has become an interdisciplinary and multidisciplinary topic. The research areas are dominated by Australia, USA, Canada, New Zealand, and European countries, which have the most productive authors and institutions. The hot topics of climate change and tourism research in recent years are further identified, including the consequences of climate change for tourism, necessary adaptations, the vulnerability of the tourism industry, tourist behaviour and demand in response to climate change, and emission reductions in the tourism sector. The work includes an in-depth analysis of a major forum of climate change and tourism to help readers to better understand global trends in this field in the past 25 years. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title="climate change">climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=tourism" title=" tourism"> tourism</a>, <a href="https://publications.waset.org/abstracts/search?q=scientometrics" title=" scientometrics"> scientometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=CiteSpace" title=" CiteSpace"> CiteSpace</a> </p> <a href="https://publications.waset.org/abstracts/59883/climate-change-and-tourism-a-scientometric-analysis-using-citespace" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59883.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">414</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">49</span> Supplier Relationship Management and Selection Strategies: A Literature Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Priyesh%20Kumar%20Singh">Priyesh Kumar Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20K.%20Sharma"> S. K. Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjay%20Verma"> Sanjay Verma</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Samuel"> C. Samuel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Supplier Relationship Management (SRM), is strategic planning and managing of all interactions with suppliers to maximize its value. Its application varies from construction industries to healthcare system and investment banks to aviation industries. Several buyer-supplier relationship models, as well as supplier selection and evaluation strategies, have been documented by many academicians and researchers. In this paper, through a comprehensive literature review of over 30 published papers, different theoretical models, empirical data and conclusions were analysed relating to SRM to find its role in establishing better supplier relationships. These journal articles were searched by using the keyword “supplier relationship management,” in databases of Mendeley Library, ProQuest, EBSCO and Google Scholar. This paper reviews the academic literature on different relationship models, supplier evaluation, and selection strategies to discuss its implications in different situations. It also describes the dominant factors responsible for buyer-supplier relationships such trust and power. Finally, conclusions have been drawn which can be validated by various researchers and can help practitioners in industries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=supplier%20relationship%20management" title="supplier relationship management">supplier relationship management</a>, <a href="https://publications.waset.org/abstracts/search?q=supplier%20performance" title=" supplier performance"> supplier performance</a>, <a href="https://publications.waset.org/abstracts/search?q=supplier%20evaluation" title=" supplier evaluation"> supplier evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=supplier%20selection%20strategies" title=" supplier selection strategies"> supplier selection strategies</a> </p> <a href="https://publications.waset.org/abstracts/84122/supplier-relationship-management-and-selection-strategies-a-literature-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84122.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">277</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=keyword&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=keyword&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=keyword&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> 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