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modeling</title> <meta name="description" content="Search results for: topic modeling"> <meta name="keywords" content="topic modeling"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="topic modeling"> <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> 5248</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: topic modeling</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5248</span> Topic Modelling Using Latent Dirichlet Allocation and Latent Semantic Indexing on SA Telco Twitter Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phumelele%20Kubheka">Phumelele Kubheka</a>, <a href="https://publications.waset.org/abstracts/search?q=Pius%20Owolawi"> Pius Owolawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Gbolahan%20Aiyetoro"> Gbolahan Aiyetoro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. Many industries such as Telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model represented in Table 1. A higher topic coherence score indicates better performance of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20Dirichlet%20allocation" title=" latent Dirichlet allocation"> latent Dirichlet allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20semantic%20indexing" title=" latent semantic indexing"> latent semantic indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=telco" title=" telco"> telco</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a> </p> <a href="https://publications.waset.org/abstracts/147818/topic-modelling-using-latent-dirichlet-allocation-and-latent-semantic-indexing-on-sa-telco-twitter-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147818.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">150</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">5247</span> Analysis of Trends in Environmental Health Research Using Topic Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hayoung%20Cho">Hayoung Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabi%20Cho"> Gabi Cho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In response to the continuing increase of demands for living environment safety, the Korean government has established and implemented various environmental health policies and set a high priority to the related R&D. However, the level of related technologies such as environmental risk assessment are still relatively low, and there is a need for detailed investment strategies in the field of environmental health research. As scientific research papers can give valuable implications on the development of a certain field, this study analyzed the global research trends in the field of environmental health over the past 10 years (2005~2015). Research topics were extracted from abstracts of the collected SCI papers using topic modeling to study the changes in research trends and discover emerging technologies. The method of topic modeling can improve the traditional bibliometric approach and provide a more comprehensive review of the global research development. The results of this study are expected to help provide insights for effective policy making and R&D investment direction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environmental%20health" title="environmental health">environmental health</a>, <a href="https://publications.waset.org/abstracts/search?q=paper%20analysis" title=" paper analysis"> paper analysis</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=topic%20modeling" title=" topic modeling"> topic modeling</a> </p> <a href="https://publications.waset.org/abstracts/50780/analysis-of-trends-in-environmental-health-research-using-topic-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50780.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">287</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">5246</span> Diversity in Finance Literature Revealed through the Lens of Machine Learning: A Topic Modeling Approach on Academic Papers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oumaima%20Lahmar">Oumaima Lahmar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to define a structured topography for finance researchers seeking to navigate the body of knowledge in their extrapolation of finance phenomena. To make sense of the body of knowledge in finance, a probabilistic topic modeling approach is applied on 6000 abstracts of academic articles published in three top journals in finance between 1976 and 2020. This approach combines both machine learning techniques and natural language processing to statistically identify the conjunctions between research articles and their shared topics described each by relevant keywords. The topic modeling analysis reveals 35 coherent topics that can well depict finance literature and provide a comprehensive structure for the ongoing research themes. Comparing the extracted topics to the Journal of Economic Literature (JEL) classification system, a significant similarity was highlighted between the characterizing keywords. On the other hand, we identify other topics that do not match the JEL classification despite being relevant in the finance literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finance%20literature" title="finance literature">finance literature</a>, <a href="https://publications.waset.org/abstracts/search?q=textual%20analysis" title=" textual analysis"> textual analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=perplexity" title=" perplexity"> perplexity</a> </p> <a href="https://publications.waset.org/abstracts/147956/diversity-in-finance-literature-revealed-through-the-lens-of-machine-learning-a-topic-modeling-approach-on-academic-papers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147956.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">170</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">5245</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">5244</span> Artificial Intelligence Assisted Sentiment Analysis of Hotel Reviews Using Topic Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sushma%20Ghogale">Sushma Ghogale</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=latent%20Dirichlet%20allocation" title="latent Dirichlet allocation">latent Dirichlet allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a> </p> <a href="https://publications.waset.org/abstracts/132279/artificial-intelligence-assisted-sentiment-analysis-of-hotel-reviews-using-topic-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132279.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">97</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">5243</span> Recognizing an Individual, Their Topic of Conversation and Cultural Background from 3D Body Movement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gheida%20J.%20Shahrour">Gheida J. Shahrour</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20J.%20Russell"> Martin J. Russell</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=person%20recognition" title="person recognition">person recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20recognition" title=" topic recognition"> topic recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=culture%20recognition" title=" culture recognition"> culture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20body%20movement%20signals" title=" 3D body movement signals"> 3D body movement signals</a>, <a href="https://publications.waset.org/abstracts/search?q=variability%20compensation" title=" variability compensation"> variability compensation</a> </p> <a href="https://publications.waset.org/abstracts/19473/recognizing-an-individual-their-topic-of-conversation-and-cultural-background-from-3d-body-movement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19473.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">541</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">5242</span> Web Search Engine Based Naming Procedure for Independent Topic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takahiro%20Nishigaki">Takahiro Nishigaki</a>, <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Onoda"> Takashi Onoda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, the number of document data has been increasing since the spread of the Internet. Many methods have been studied for extracting topics from large document data. We proposed Independent Topic Analysis (ITA) to extract topics independent of each other from large document data such as newspaper data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis. The topic represented by ITA is represented by a set of words. However, the set of words is quite different from the topics the user imagines. For example, the top five words with high independence of a topic are as follows. Topic1 = {"scor", "game", "lead", "quarter", "rebound"}. This Topic 1 is considered to represent the topic of "SPORTS". This topic name "SPORTS" has to be attached by the user. ITA cannot name topics. Therefore, in this research, we propose a method to obtain topics easy for people to understand by using the web search engine, topics given by the set of words given by independent topic analysis. In particular, we search a set of topical words, and the title of the homepage of the search result is taken as the topic name. And we also use the proposed method for some data and verify its effectiveness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=independent%20topic%20analysis" title="independent topic analysis">independent topic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20extraction" title=" topic extraction"> topic extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20naming" title=" topic naming"> topic naming</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20search%20engine" title=" web search engine"> web search engine</a> </p> <a href="https://publications.waset.org/abstracts/98583/web-search-engine-based-naming-procedure-for-independent-topic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98583.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">119</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">5241</span> Towards Law Data Labelling Using Topic Modelling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Pinheiro%20Da%20Silva%20Junior">Daniel Pinheiro Da Silva Junior</a>, <a href="https://publications.waset.org/abstracts/search?q=Aline%20Paes"> Aline Paes</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20De%20Oliveira"> Daniel De Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Christiano%20Lacerda%20Ghuerren"> Christiano Lacerda Ghuerren</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcio%20Duran"> Marcio Duran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Courts of Accounts are institutions responsible for overseeing and point out irregularities of Public Administration expenses. They have a high demand for processes to be analyzed, whose decisions must be grounded on severity laws. Despite the existing large amount of processes, there are several cases reporting similar subjects. Thus, previous decisions on already analyzed processes can be a precedent for current processes that refer to similar topics. Identifying similar topics is an open, yet essential task for identifying similarities between several processes. Since the actual amount of topics is considerably large, it is tedious and error-prone to identify topics using a pure manual approach. This paper presents a tool based on Machine Learning and Natural Language Processing to assists in building a labeled dataset. The tool relies on Topic Modelling with Latent Dirichlet Allocation to find the topics underlying a document followed by Jensen Shannon distance metric to generate a probability of similarity between documents pairs. Furthermore, in a case study with a corpus of decisions of the Rio de Janeiro State Court of Accounts, it was noted that data pre-processing plays an essential role in modeling relevant topics. Also, the combination of topic modeling and a calculated distance metric over document represented among generated topics has been proved useful in helping to construct a labeled base of similar and non-similar document pairs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=courts%20of%20accounts" title="courts of accounts">courts of accounts</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20labelling" title=" data labelling"> data labelling</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20similarity" title=" document similarity"> document similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a> </p> <a href="https://publications.waset.org/abstracts/121281/towards-law-data-labelling-using-topic-modelling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121281.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">179</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">5240</span> Investigation of Topic Modeling-Based Semi-Supervised Interpretable Document Classifier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dasom%20Kim">Dasom Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=William%20Xiu%20Shun%20Wong"> William Xiu Shun Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoonjin%20Hyun"> Yoonjin Hyun</a>, <a href="https://publications.waset.org/abstracts/search?q=Donghoon%20Lee"> Donghoon Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Minji%20Paek"> Minji Paek</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungho%20Byun"> Sungho Byun</a>, <a href="https://publications.waset.org/abstracts/search?q=Namgyu%20Kim"> Namgyu Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There have been many researches on document classification for classifying voluminous documents automatically. Through document classification, we can assign a specific category to each unlabeled document on the basis of various machine learning algorithms. However, providing labeled documents manually requires considerable time and effort. To overcome the limitations, the semi-supervised learning which uses unlabeled document as well as labeled documents has been invented. However, traditional document classifiers, regardless of supervised or semi-supervised ones, cannot sufficiently explain the reason or the process of the classification. Thus, in this paper, we proposed a methodology to visualize major topics and class components of each document. We believe that our methodology for visualizing topics and classes of each document can enhance the reliability and explanatory power of document classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20classifier" title=" document classifier"> document classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a> </p> <a href="https://publications.waset.org/abstracts/48985/investigation-of-topic-modeling-based-semi-supervised-interpretable-document-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48985.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">402</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">5239</span> Visualization and Performance Measure to Determine Number of Topics in Twitter Data Clustering Using Hybrid Topic Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moulana%20Mohammed">Moulana Mohammed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring in choosing optimal number of topics. The main problem is the lack of a stable metric of the quality of topics obtained during the construction of topic models. The authors analyzed from previous works, most of the models used in determining the number of topics are non-parametric and quality of topics determined by using perplexity and coherence measures and concluded that they are not applicable in solving this problem. In this paper, we used the parametric method, which is an extension of the traditional topic model with visual access tendency for visualization of the number of topics (clusters) to complement clustering and to choose optimal number of topics based on results of cluster validity indices. Developed hybrid topic models are demonstrated with different Twitter datasets on various topics in obtaining the optimal number of topics and in measuring the quality of clusters. The experimental results showed that the Visual Non-negative Matrix Factorization (VNMF) topic model performs well in determining the optimal number of topics with interactive visualization and in performance measure of the quality of clusters with validity indices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interactive%20visualization" title="interactive visualization">interactive visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20mon-negative%20matrix%20factorization%20model" title=" visual mon-negative matrix factorization model"> visual mon-negative matrix factorization model</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20number%20of%20topics" title=" optimal number of topics"> optimal number of topics</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20validity%20indices" title=" cluster validity indices"> cluster validity indices</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter%20data%20clustering" title=" Twitter data clustering"> Twitter data clustering</a> </p> <a href="https://publications.waset.org/abstracts/124453/visualization-and-performance-measure-to-determine-number-of-topics-in-twitter-data-clustering-using-hybrid-topic-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124453.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">134</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">5238</span> Unsupervised Text Mining Approach to Early Warning System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ichihan%20Tai">Ichihan Tai</a>, <a href="https://publications.waset.org/abstracts/search?q=Bill%20Olson"> Bill Olson</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Blessner"> Paul Blessner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=early%20warning%20system" title="early warning system">early warning system</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20prediction" title=" market prediction"> market prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling." title=" topic modeling."> topic modeling.</a> </p> <a href="https://publications.waset.org/abstracts/46013/unsupervised-text-mining-approach-to-early-warning-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46013.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">5237</span> Emotion Oriented Students' Opinioned Topic Detection for Course Reviews in Massive Open Online Course</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhi%20Liu">Zhi Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xian%20Peng"> Xian Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Monika%20Domanska"> Monika Domanska</a>, <a href="https://publications.waset.org/abstracts/search?q=Lingyun%20Kang"> Lingyun Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sannyuya%20Liu"> Sannyuya Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Massive Open education has become increasingly popular among worldwide learners. An increasing number of course reviews are being generated in Massive Open Online Course (MOOC) platform, which offers an interactive feedback channel for learners to express opinions and feelings in learning. These reviews typically contain subjective emotion and topic information towards the courses. However, it is time-consuming to artificially detect these opinions. In this paper, we propose an emotion-oriented topic detection model to automatically detect the students’ opinioned aspects in course reviews. The known overall emotion orientation and emotional words in each review are used to guide the joint probabilistic modeling of emotion and aspects in reviews. Through the experiment on real-life review data, it is verified that the distribution of course-emotion-aspect can be calculated to capture the most significant opinioned topics in each course unit. This proposed technique helps in conducting intelligent learning analytics for teachers to improve pedagogies and for developers to promote user experiences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Massive%20Open%20Online%20Course%20%28MOOC%29" title="Massive Open Online Course (MOOC)">Massive Open Online Course (MOOC)</a>, <a href="https://publications.waset.org/abstracts/search?q=course%20reviews" title=" course reviews"> course reviews</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20model" title=" topic model"> topic model</a>, <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=topical%20aspects" title=" topical aspects"> topical aspects</a> </p> <a href="https://publications.waset.org/abstracts/86771/emotion-oriented-students-opinioned-topic-detection-for-course-reviews-in-massive-open-online-course" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86771.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">5236</span> Topic Sentiments toward the COVID-19 Vaccine on Twitter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Melissa%20Vang">Melissa Vang</a>, <a href="https://publications.waset.org/abstracts/search?q=Raheyma%20Khan"> Raheyma Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Haihua%20Chen"> Haihua Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The coronavirus disease 2019 (COVID‐19) pandemic has changed people's lives from all over the world. More people have turned to Twitter to engage online and discuss the COVID-19 vaccine. This study aims to present a text mining approach to identify people's attitudes towards the COVID-19 vaccine on Twitter. To achieve this purpose, we collected 54,268 COVID-19 vaccine tweets from September 01, 2020, to November 01, 2020, then the BERT model is used for the sentiment and topic analysis. The results show that people had more negative than positive attitudes about the vaccine, and countries with an increasing number of confirmed cases had a higher percentage of negative attitudes. Additionally, the topics discussed in positive and negative tweets are different. The tweet datasets can be helpful to information professionals to inform the public about vaccine-related informational resources. Our findings may have implications for understanding people's cognitions and feelings about the vaccine. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BERT" title="BERT">BERT</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19%20vaccine" title=" COVID-19 vaccine"> COVID-19 vaccine</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=topic%20modeling" title=" topic modeling"> topic modeling</a> </p> <a href="https://publications.waset.org/abstracts/138813/topic-sentiments-toward-the-covid-19-vaccine-on-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138813.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">150</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">5235</span> Off-Topic Text Detection System Using a Hybrid Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Usama%20Shahid">Usama Shahid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=off%20topic" title="off topic">off topic</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20detection" title=" text detection"> text detection</a>, <a href="https://publications.waset.org/abstracts/search?q=eco%20state%20network" title=" eco state network"> eco state network</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/160685/off-topic-text-detection-system-using-a-hybrid-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160685.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">85</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">5234</span> Improving Topic Quality of Scripts by Using Scene Similarity Based Word Co-Occurrence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yunseok%20Noh">Yunseok Noh</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang-Uk%20Kwak"> Chang-Uk Kwak</a>, <a href="https://publications.waset.org/abstracts/search?q=Sun-Joong%20Kim"> Sun-Joong Kim</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> Scripts are one of the basic text resources to understand broadcasting contents. Since broadcast media wields lots of influence over the public, tools for understanding broadcasting contents are more required. Topic modeling is the method to get the summary of the broadcasting contents from its scripts. Generally, scripts represent contents descriptively with directions and speeches. Scripts also provide scene segments that can be seen as semantic units. Therefore, a script can be topic modeled by treating a scene segment as a document. Because scripts consist of speeches mainly, however, relatively small co-occurrences among words in the scene segments are observed. This causes inevitably the bad quality of topics based on statistical learning method. To tackle this problem, we propose a method of learning with additional word co-occurrence information obtained using scene similarities. The main idea of improving topic quality is that the information that two or more texts are topically related can be useful to learn high quality of topics. In addition, by using high quality of topics, we can get information more accurate whether two texts are related or not. In this paper, we regard two scene segments are related if their topical similarity is high enough. We also consider that words are co-occurred if they are in topically related scene segments together. In the experiments, we showed the proposed method generates a higher quality of topics from Korean drama scripts than the baselines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=broadcasting%20contents" title="broadcasting contents">broadcasting contents</a>, <a href="https://publications.waset.org/abstracts/search?q=scripts" title=" scripts"> scripts</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20similarity" title=" text similarity"> text similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20model" title=" topic model"> topic model</a> </p> <a href="https://publications.waset.org/abstracts/43196/improving-topic-quality-of-scripts-by-using-scene-similarity-based-word-co-occurrence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43196.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">318</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">5233</span> Scientific Recommender Systems Based on Neural Topic Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Smail%20Boussaadi">Smail Boussaadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassina%20Aliane"> Hassina Aliane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid growth of scientific literature, it is becoming increasingly challenging for researchers to keep up with the latest findings in their fields. Academic, professional networks play an essential role in connecting researchers and disseminating knowledge. To improve the user experience within these networks, we need effective article recommendation systems that provide personalized content.Current recommendation systems often rely on collaborative filtering or content-based techniques. However, these methods have limitations, such as the cold start problem and difficulty in capturing semantic relationships between articles. To overcome these challenges, we propose a new approach that combines BERTopic (Bidirectional Encoder Representations from Transformers), a state-of-the-art topic modeling technique, with community detection algorithms in a academic, professional network. Experiences confirm our performance expectations by showing good relevance and objectivity in the results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=scientific%20articles" title="scientific articles">scientific articles</a>, <a href="https://publications.waset.org/abstracts/search?q=community%20detection" title=" community detection"> community detection</a>, <a href="https://publications.waset.org/abstracts/search?q=academic%20social%20network" title=" academic social network"> academic social network</a>, <a href="https://publications.waset.org/abstracts/search?q=recommender%20systems" title=" recommender systems"> recommender systems</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20topic%20model" title=" neural topic model"> neural topic model</a> </p> <a href="https://publications.waset.org/abstracts/165792/scientific-recommender-systems-based-on-neural-topic-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165792.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">97</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">5232</span> Topic-to-Essay Generation with Event Element Constraints</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yufen%20Qin">Yufen Qin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Topic-to-Essay generation is a challenging task in Natural language processing, which aims to generate novel, diverse, and topic-related text based on user input. Previous research has overlooked the generation of articles under the constraints of event elements, resulting in issues such as incomplete event elements and logical inconsistencies in the generated results. To fill this gap, this paper proposes an event-constrained approach for a topic-to-essay generation that enforces the completeness of event elements during the generation process. Additionally, a language model is employed to verify the logical consistency of the generated results. Experimental results demonstrate that the proposed model achieves a better BLEU-2 score and performs better than the baseline in terms of subjective evaluation on a real dataset, indicating its capability to generate higher-quality topic-related text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=event%20element" title="event element">event element</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20model" title=" language model"> language model</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=topic-to-essay%20generation." title=" topic-to-essay generation."> topic-to-essay generation.</a> </p> <a href="https://publications.waset.org/abstracts/168393/topic-to-essay-generation-with-event-element-constraints" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168393.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">236</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">5231</span> Trend Detection Using Community Rank and Hawkes Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shashank%20Bhatnagar">Shashank Bhatnagar</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Wilfred%20Godfrey"> W. Wilfred Godfrey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We develop in this paper, an approach to find the trendy topic, which not only considers the user-topic interaction but also considers the community, in which user belongs. This method modifies the previous approach of user-topic interaction to user-community-topic interaction with better speed-up in the range of [1.1-3]. We assume that trend detection in a social network is dependent on two things. The one is, broadcast of messages in social network governed by self-exciting point process, namely called Hawkes process and the second is, Community Rank. The influencer node links to others in the community and decides the community rank based on its PageRank and the number of users links to that community. The community rank decides the influence of one community over the other. Hence, the Hawkes process with the kernel of user-community-topic decides the trendy topic disseminated into the social network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=community%20detection" title="community detection">community detection</a>, <a href="https://publications.waset.org/abstracts/search?q=community%20rank" title=" community rank"> community rank</a>, <a href="https://publications.waset.org/abstracts/search?q=Hawkes%20process" title=" Hawkes process"> Hawkes process</a>, <a href="https://publications.waset.org/abstracts/search?q=influencer%20node" title=" influencer node"> influencer node</a>, <a href="https://publications.waset.org/abstracts/search?q=pagerank" title=" pagerank"> pagerank</a>, <a href="https://publications.waset.org/abstracts/search?q=trend%20detection" title=" trend detection"> trend detection</a> </p> <a href="https://publications.waset.org/abstracts/73595/trend-detection-using-community-rank-and-hawkes-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73595.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">383</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">5230</span> A Rapid and Cost-Effective Approach to Manufacturing Modeling Platform for Fused Deposition Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chil-Chyuan%20Kuo">Chil-Chyuan Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen-Hsuan%20Tsai"> Chen-Hsuan Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents a cost-effective approach for rapid fabricating modeling platforms utilized in fused deposition modeling system. A small-batch production of modeling platforms about 20 pieces can be obtained economically through silicone rubber mold using vacuum casting without applying the plastic injection molding. The air venting systems is crucial for fabricating modeling platform using vacuum casting. Modeling platforms fabricated can be used for building rapid prototyping model after sandblasting. This study offers industrial value because it has both time-effectiveness and cost-effectiveness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vacuum%20casting" title="vacuum casting">vacuum casting</a>, <a href="https://publications.waset.org/abstracts/search?q=fused%20deposition%20modeling" title=" fused deposition modeling"> fused deposition modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling%20platform" title=" modeling platform"> modeling platform</a>, <a href="https://publications.waset.org/abstracts/search?q=sandblasting" title=" sandblasting"> sandblasting</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a> </p> <a href="https://publications.waset.org/abstracts/8812/a-rapid-and-cost-effective-approach-to-manufacturing-modeling-platform-for-fused-deposition-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8812.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">382</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">5229</span> Standardized Description and Modeling Methods of Semiconductor IP Interfaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seongsoo%20Lee">Seongsoo Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> IP reuse is an effective design methodology for modern SoC design to reduce effort and time. However, description and modeling methods of IP interfaces are different due to different IP designers. In this paper, standardized description and modeling methods of IP interfaces are proposed. It consists of 11 items such as IP information, model provision, data type, description level, interface information, port information, signal information, protocol information, modeling level, modeling information, and source file. The proposed description and modeling methods enables easy understanding, simulation, verification, and modification in IP reuse. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interface" title="interface">interface</a>, <a href="https://publications.waset.org/abstracts/search?q=standardization" title=" standardization"> standardization</a>, <a href="https://publications.waset.org/abstracts/search?q=description" title=" description"> description</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=semiconductor%20IP" title=" semiconductor IP"> semiconductor IP</a> </p> <a href="https://publications.waset.org/abstracts/16150/standardized-description-and-modeling-methods-of-semiconductor-ip-interfaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16150.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">502</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">5228</span> Characterization of Group Dynamics for Fostering Mathematical Modeling Competencies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayse%20Ozturk">Ayse Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study extends the prior research on modeling competencies by positioning students’ cognitive and language resources as the fundamentals for pursuing their own inquiry and expression lines through mathematical modeling. This strategy aims to answer the question that guides this study, “How do students’ group approaches to modeling tasks affect their modeling competencies over a unit of instruction?” Six bilingual tenth-grade students worked on open-ended modeling problems along with the content focused on quantities over six weeks. Each group was found to have a unique cognitive approach for solving these problems. Three different problem-solving strategies affected how the groups’ modeling competencies changed. The results provide evidence that the discussion around groups’ solutions, coupled with their reflections, advances group interpreting and validating competencies in the mathematical modeling process <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognition" title="cognition">cognition</a>, <a href="https://publications.waset.org/abstracts/search?q=collective%20learning" title=" collective learning"> collective learning</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20modeling%20competencies" title=" mathematical modeling competencies"> mathematical modeling competencies</a>, <a href="https://publications.waset.org/abstracts/search?q=problem-solving" title=" problem-solving"> problem-solving</a> </p> <a href="https://publications.waset.org/abstracts/146619/characterization-of-group-dynamics-for-fostering-mathematical-modeling-competencies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146619.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">158</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">5227</span> Knowledge Representation and Inconsistency Reasoning of Class Diagram Maintenance in Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chi-Lun%20Liu">Chi-Lun Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Requirements modeling and analysis are important in successful information systems' maintenance. Unified Modeling Language (UML) class diagrams are useful standards for modeling information systems. To our best knowledge, there is a lack of a systems development methodology described by the organism metaphor. The core concept of this metaphor is adaptation. Using the knowledge representation and reasoning approach and ontologies to adopt new requirements are emergent in recent years. This paper proposes an organic methodology which is based on constructivism theory. This methodology is a knowledge representation and reasoning approach to analyze new requirements in the class diagrams maintenance. The process and rules in the proposed methodology automatically analyze inconsistencies in the class diagram. In the big data era, developing an automatic tool based on the proposed methodology to analyze large amounts of class diagram data is an important research topic in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20representation" title="knowledge representation">knowledge representation</a>, <a href="https://publications.waset.org/abstracts/search?q=reasoning" title=" reasoning"> reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=class%20diagram" title=" class diagram"> class diagram</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20engineering" title=" software engineering"> software engineering</a> </p> <a href="https://publications.waset.org/abstracts/93116/knowledge-representation-and-inconsistency-reasoning-of-class-diagram-maintenance-in-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93116.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">241</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">5226</span> Text Mining of Twitter Data Using a Latent Dirichlet Allocation Topic Model and Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sidi%20Yang">Sidi Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Haiyi%20Zhang"> Haiyi Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is a microblogging platform, where millions of users daily share their attitudes, views, and opinions. Using a probabilistic Latent Dirichlet Allocation (LDA) topic model to discern the most popular topics in the Twitter data is an effective way to analyze a large set of tweets to find a set of topics in a computationally efficient manner. Sentiment analysis provides an effective method to show the emotions and sentiments found in each tweet and an efficient way to summarize the results in a manner that is clearly understood. The primary goal of this paper is to explore text mining, extract and analyze useful information from unstructured text using two approaches: LDA topic modelling and sentiment analysis by examining Twitter plain text data in English. These two methods allow people to dig data more effectively and efficiently. LDA topic model and sentiment analysis can also be applied to provide insight views in business and scientific fields. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title="text mining">text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20model" title=" topic model"> topic model</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a> </p> <a href="https://publications.waset.org/abstracts/95281/text-mining-of-twitter-data-using-a-latent-dirichlet-allocation-topic-model-and-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95281.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">179</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">5225</span> Bridging the Gap between Different Interfaces for Business Process Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katalina%20Grigorova">Katalina Grigorova</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaloyan%20Mironov"> Kaloyan Mironov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper focuses on the benefits of business process modeling. Although this discipline is developing for many years, there is still necessity of creating new opportunities to meet the ever-increasing users’ needs. Because one of these needs is related to the conversion of business process models from one standard to another, the authors have developed a converter between BPMN and EPC standards using workflow patterns as intermediate tool. Nowadays there are too many systems for business process modeling. The variety of output formats is almost the same as the systems themselves. This diversity additionally hampers the conversion of the models. The presented study is aimed at discussing problems due to differences in the output formats of various modeling environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20process%20modeling" title="business process modeling">business process modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20process%20modeling%20standards" title=" business process modeling standards"> business process modeling standards</a>, <a href="https://publications.waset.org/abstracts/search?q=workflow%20patterns" title=" workflow patterns"> workflow patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=converting%20models" title=" converting models"> converting models</a> </p> <a href="https://publications.waset.org/abstracts/40931/bridging-the-gap-between-different-interfaces-for-business-process-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40931.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">585</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">5224</span> Commercial Automobile Insurance: A Practical Approach of the Generalized Additive Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nicolas%20Plamondon">Nicolas Plamondon</a>, <a href="https://publications.waset.org/abstracts/search?q=Stuart%20Atkinson"> Stuart Atkinson</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuzi%20Zhou"> Shuzi Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The insurance industry is usually not the first topic one has in mind when thinking about applications of data science. However, the use of data science in the finance and insurance industry is growing quickly for several reasons, including an abundance of reliable customer data, ferocious competition requiring more accurate pricing, etc. Among the top use cases of data science, we find pricing optimization, customer segmentation, customer risk assessment, fraud detection, marketing, and triage analytics. The objective of this paper is to present an application of the generalized additive model (GAM) on a commercial automobile insurance product: an individually rated commercial automobile. These are vehicles used for commercial purposes, but for which there is not enough volume to apply pricing to several vehicles at the same time. The GAM model was selected as an improvement over GLM for its ease of use and its wide range of applications. The model was trained using the largest split of the data to determine model parameters. The remaining part of the data was used as testing data to verify the quality of the modeling activity. We used the Gini coefficient to evaluate the performance of the model. For long-term monitoring, commonly used metrics such as RMSE and MAE will be used. Another topic of interest in the insurance industry is to process of producing the model. We will discuss at a high level the interactions between the different teams with an insurance company that needs to work together to produce a model and then monitor the performance of the model over time. Moreover, we will discuss the regulations in place in the insurance industry. Finally, we will discuss the maintenance of the model and the fact that new data does not come constantly and that some metrics can take a long time to become meaningful. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=insurance" title="insurance">insurance</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20science" title=" data science"> data science</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=regulation" title=" regulation"> regulation</a>, <a href="https://publications.waset.org/abstracts/search?q=processes" title=" processes"> processes</a> </p> <a href="https://publications.waset.org/abstracts/159487/commercial-automobile-insurance-a-practical-approach-of-the-generalized-additive-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159487.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">76</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">5223</span> Optimized Text Summarization Model on Mobile Screens for Sight-Interpreters: An Empirical Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianhua%20Wang">Jianhua Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To obtain key information quickly from long texts on small screens of mobile devices, sight-interpreters need to establish optimized summarization model for fast information retrieval. Four summarization models based on previous studies were studied including title+key words (TKW), title+topic sentences (TTS), key words+topic sentences (KWTS) and title+key words+topic sentences (TKWTS). Psychological experiments were conducted on the four models for three different genres of interpreting texts to establish the optimized summarization model for sight-interpreters. This empirical study shows that the optimized summarization model for sight-interpreters to quickly grasp the key information of the texts they interpret is title+key words (TKW) for cultural texts, title+key words+topic sentences (TKWTS) for economic texts and topic sentences+key words (TSKW) for political texts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=different%20genres" title="different genres">different genres</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20screens" title=" mobile screens"> mobile screens</a>, <a href="https://publications.waset.org/abstracts/search?q=optimized%20summarization%20models" title=" optimized summarization models"> optimized summarization models</a>, <a href="https://publications.waset.org/abstracts/search?q=sight-interpreters" title=" sight-interpreters"> sight-interpreters</a> </p> <a href="https://publications.waset.org/abstracts/57345/optimized-text-summarization-model-on-mobile-screens-for-sight-interpreters-an-empirical-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57345.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">314</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">5222</span> Using Bidirectional Encoder Representations from Transformers to Extract Topic-Independent Sentiment Features for Social Media Bot Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Heidari">Maryam Heidari</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20H.%20Jones%20Jr."> James H. Jones Jr.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bot%20detection" title="bot detection">bot detection</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=neural%20network" title=" neural network"> neural network</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/129049/using-bidirectional-encoder-representations-from-transformers-to-extract-topic-independent-sentiment-features-for-social-media-bot-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129049.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">116</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">5221</span> Revolutionary Solutions for Modeling and Visualization of Complex Software Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jay%20Xiong">Jay Xiong</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Lin"> Li Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Existing software modeling and visualization approaches using UML are outdated, which are outcomes of reductionism and the superposition principle that the whole of a system is the sum of its parts, so that with them all tasks of software modeling and visualization are performed linearly, partially, and locally. This paper introduces revolutionary solutions for modeling and visualization of complex software systems, which make complex software systems much easy to understand, test, and maintain. The solutions are based on complexity science, offering holistic, automatic, dynamic, virtual, and executable approaches about thousand times more efficient than the traditional ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complex%20systems" title="complex systems">complex systems</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20maintenance" title=" software maintenance"> software maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20modeling" title=" software modeling"> software modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20visualization" title=" software visualization"> software visualization</a> </p> <a href="https://publications.waset.org/abstracts/41451/revolutionary-solutions-for-modeling-and-visualization-of-complex-software-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41451.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">401</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">5220</span> Effects of Topic Familiarity on Linguistic Aspects in EFL Learners’ Writing Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeong-Won%20Lee">Jeong-Won Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyeong-Ok%20Yoon"> Kyeong-Ok Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The current study aimed to investigate the effects of topic familiarity and language proficiency on linguistic aspects (lexical complexity, syntactic complexity, accuracy, and fluency) in EFL learners’ argumentative essays. For the study 64 college students were asked to write an argumentative essay for the two different topics (Driving and Smoking) chosen by the consideration of topic familiarity. The students were divided into two language proficiency groups (high-level and intermediate) according to their English writing proficiency. The findings of the study are as follows: 1) the participants of this study exhibited lower levels of lexical and syntactic complexity as well as accuracy when performing writing tasks with unfamiliar topics; and 2) they demonstrated the use of a wider range of vocabulary, and longer and more complex structures, and produced accurate and lengthier texts compared to their intermediate peers. Discussion and pedagogical implications for instruction of writing classes in EFL contexts were addressed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=topic%20familiarity" title="topic familiarity">topic familiarity</a>, <a href="https://publications.waset.org/abstracts/search?q=complexity" title=" complexity"> complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=fluency" title=" fluency"> fluency</a> </p> <a href="https://publications.waset.org/abstracts/182005/effects-of-topic-familiarity-on-linguistic-aspects-in-efl-learners-writing-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182005.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">50</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">5219</span> Application Water Quality Modelling In Total Maximum Daily Load (TMDL) Management: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Che%20Osmi">S. A. Che Osmi</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20M.%20F.%20W.%20Ishak"> W. M. F. W. Ishak</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20F.%20Che%20Osmi"> S. F. Che Osmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays the issues of water quality and water pollution have been a major problem across the country. A lot of management attempt to develop their own TMDL database in order to control the river pollution. Over the past decade, the mathematical modeling has been used as the tool for the development of TMDL. This paper presents the application of water quality modeling to develop the total maximum daily load (TMDL) information. To obtain the reliable database of TMDL, the appropriate water quality modeling should choose based on the available data provided. This paper will discuss on the use of several water quality modeling such as QUAL2E, QUAL2K, and EFDC to develop TMDL. The attempts to integrate several modeling are also being discussed in this paper. Based on this paper, the differences in the application of water quality modeling based on their properties such as one, two or three dimensional are showing their ability to develop the modeling of TMDL database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=TMDL" title="TMDL">TMDL</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20modeling" title=" water quality modeling"> water quality modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=QUAL2E" title=" QUAL2E"> QUAL2E</a>, <a href="https://publications.waset.org/abstracts/search?q=EFDC" title=" EFDC"> EFDC</a> </p> <a href="https://publications.waset.org/abstracts/38187/application-water-quality-modelling-in-total-maximum-daily-load-tmdl-management-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38187.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">439</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=topic%20modeling&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=topic%20modeling&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=topic%20modeling&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=topic%20modeling&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=topic%20modeling&page=6">6</a></li> <li class="page-item"><a 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