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Search results for: investor sentiment

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</div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: investor sentiment</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">347</span> Exposing Investor Sentiment In Stock Returns</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiang%20Bu">Qiang Bu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper compares the explanatory power of sentiment level and sentiment shock. The preliminary test results show that sentiment shock plays a more significant role in explaining stocks returns, including the raw return and abnormal return. We also find that sentiment shock beta has a higher statistical significance than sentiment beta. These finding sheds new light on the relationship between investor sentiment and stock returns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20level" title="sentiment level">sentiment level</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20shock" title=" sentiment shock"> sentiment shock</a>, <a href="https://publications.waset.org/abstracts/search?q=explanatory%20power" title=" explanatory power"> explanatory power</a>, <a href="https://publications.waset.org/abstracts/search?q=abnormal%20stock%20return" title=" abnormal stock return"> abnormal stock return</a>, <a href="https://publications.waset.org/abstracts/search?q=beta" title=" beta"> beta</a> </p> <a href="https://publications.waset.org/abstracts/146061/exposing-investor-sentiment-in-stock-returns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146061.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">137</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">346</span> Investor Sentiment and Commodity Trading Advisor Fund Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tian%20Lan">Tian Lan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Arbitrageurs participate in a variety of techniques in response to the existence of fluctuating sentiment, resulting in sparse sentiment exposures. This paper found that Commodity Trading Advisor (CTA) funds in the top decile rated by sentiment beta outperformed those in the bottom decile by 0.33% per month on a risk-adjusted basis, with the difference being larger among skilled managers. This paper also discovered that around ten percent of Commodity Trading Advisor (CTA) funds could accurately predict market sentiment, which has a positive correlation with fund sentiment beta and acts as a determinant in fund performance. Instead of betting against mispricing, this research demonstrates that a competent manager can achieve remarkable returns by forecasting and reacting to shifts in investor sentiment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=investment%20sentiment" title="investment sentiment">investment sentiment</a>, <a href="https://publications.waset.org/abstracts/search?q=CTA%20fund" title=" CTA fund"> CTA fund</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20timing" title=" market timing"> market timing</a>, <a href="https://publications.waset.org/abstracts/search?q=fund%20performance" title=" fund performance"> fund performance</a> </p> <a href="https://publications.waset.org/abstracts/163072/investor-sentiment-and-commodity-trading-advisor-fund-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163072.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">84</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">345</span> Investor Sentiment and Satisfaction in Automated Investment: A Sentimental Analysis of Robo-Advisor Platforms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vertika%20Goswami">Vertika Goswami</a>, <a href="https://publications.waset.org/abstracts/search?q=Gargi%20Sharma"> Gargi Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid evolution of fintech has led to the rise of robo-advisor platforms that utilize artificial intelligence (AI) and machine learning to offer personalized investment solutions efficiently and cost-effectively. This research paper conducts a comprehensive sentiment analysis of investor experiences with these platforms, employing natural language processing (NLP) and sentiment classification techniques. The study investigates investor perceptions, engagement, and satisfaction, identifying key drivers of positive sentiment such as clear communication, low fees, consistent returns, and robust security. Conversely, negative sentiment is linked to issues like inconsistent performance, hidden fees, poor customer support, and a lack of transparency. The analysis reveals that addressing these pain points—through improved transparency, enhanced customer service, and ongoing technological advancements—can significantly boost investor trust and satisfaction. This paper contributes valuable insights into the fields of behavioral finance and fintech innovation, offering actionable recommendations for stakeholders, practitioners, and policymakers. Future research should explore the long-term impact of these factors on investor loyalty, the role of emerging technologies, and the effects of ethical investment choices and regulatory compliance on investor sentiment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence%20in%20finance" title="artificial intelligence in finance">artificial intelligence in finance</a>, <a href="https://publications.waset.org/abstracts/search?q=automated%20investment" title=" automated investment"> automated investment</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20technology" title=" financial technology"> financial technology</a>, <a href="https://publications.waset.org/abstracts/search?q=investor%20satisfaction" title=" investor satisfaction"> investor satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=investor%20sentiment" title=" investor sentiment"> investor sentiment</a>, <a href="https://publications.waset.org/abstracts/search?q=robo-advisors" title=" robo-advisors"> robo-advisors</a>, <a href="https://publications.waset.org/abstracts/search?q=sentimental%20analysis" title=" sentimental analysis"> sentimental analysis</a> </p> <a href="https://publications.waset.org/abstracts/192185/investor-sentiment-and-satisfaction-in-automated-investment-a-sentimental-analysis-of-robo-advisor-platforms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192185.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">18</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">344</span> Forecast Dispersion, Investor Sentiment and the Cross Section of Stock Returns</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guoyu%20Lin">Guoyu Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores the role investor sentiment plays in the relationship between analyst forecast dispersion and stock returns. With short sale constraints, stock prices are determined by the optimistic investors. During the high sentiment periods when investors suffer more from psychological bias, there are more optimistic investors. This is the first paper to document that following the high sentiment periods, stocks with the most analyst forecast dispersion are overpriced, earning significantly negative returns, while those with the least analyst forecast dispersion are not overpriced as the degree of belief dispersion is low. However, following the low sentiment periods, both are not overpriced. A portfolio which longs the least dispersed stocks and shorts the most dispersed stocks yields significantly positive returns only following the high sentiment periods. My findings can potentially reconcile the puzzling risk effect and mispricing effect in the literature. The risk (mispricing) effect suggests a positive (negative) relation between analyst forecast dispersion and future stock returns. Presumably, the magnitude of the mispricing effect depends on the proportion of irrational investors and their bias, which is positively related to investor sentiment. During the high sentiment period, the mispricing effect takes over and the overall effect is negative. During the low sentiment period, the percentage of irrational investors is mediate, and the mispricing effect and the risk effect counter each other, leading to insignificant relation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analyst%20forecast%20dispersion" title="analyst forecast dispersion">analyst forecast dispersion</a>, <a href="https://publications.waset.org/abstracts/search?q=short-sale%20constraints" title=" short-sale constraints"> short-sale constraints</a>, <a href="https://publications.waset.org/abstracts/search?q=investor%20sentiment" title=" investor sentiment"> investor sentiment</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20returns" title=" stock returns"> stock returns</a> </p> <a href="https://publications.waset.org/abstracts/140762/forecast-dispersion-investor-sentiment-and-the-cross-section-of-stock-returns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140762.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">143</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">343</span> The Study on the Relationship between Momentum Profits and Psychological Factors: Evidence from Taiwan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chih-Hsiang%20Chang">Chih-Hsiang Chang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study provides insight into the effects of investor sentiment, excess optimism, overconfidence, the disposition effect, and herding formation on momentum profits. This study contributes to the field by providing a further examination of the relationship between psychological factors and momentum profits. The empirical results show that there is no evidence of significant momentum profits in Taiwan&rsquo;s stock market. Additionally, investor sentiment in Taiwan&rsquo;s stock market significantly influences its momentum profits. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=momentum%20profits" title="momentum profits">momentum profits</a>, <a href="https://publications.waset.org/abstracts/search?q=psychological%20factors" title=" psychological factors"> psychological factors</a>, <a href="https://publications.waset.org/abstracts/search?q=herding%20formation" title=" herding formation"> herding formation</a>, <a href="https://publications.waset.org/abstracts/search?q=investor%20sentiment" title=" investor sentiment"> investor sentiment</a> </p> <a href="https://publications.waset.org/abstracts/56302/the-study-on-the-relationship-between-momentum-profits-and-psychological-factors-evidence-from-taiwan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56302.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">380</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">342</span> Lexicon-Based Sentiment Analysis for Stock Movement Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zane%20Turner">Zane Turner</a>, <a href="https://publications.waset.org/abstracts/search?q=Kevin%20Labille"> Kevin Labille</a>, <a href="https://publications.waset.org/abstracts/search?q=Susan%20Gauch"> Susan Gauch</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computational%20finance" title="computational finance">computational finance</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=sentiment%20lexicon" title=" sentiment lexicon"> sentiment lexicon</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20movement%20prediction" title=" stock movement prediction"> stock movement prediction</a> </p> <a href="https://publications.waset.org/abstracts/127332/lexicon-based-sentiment-analysis-for-stock-movement-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127332.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">127</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">341</span> Lexicon-Based Sentiment Analysis for Stock Movement Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zane%20Turner">Zane Turner</a>, <a href="https://publications.waset.org/abstracts/search?q=Kevin%20Labille"> Kevin Labille</a>, <a href="https://publications.waset.org/abstracts/search?q=Susan%20Gauch"> Susan Gauch</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computational%20finance" title="computational finance">computational finance</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=sentiment%20lexicon" title=" sentiment lexicon"> sentiment lexicon</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20movement%20prediction" title=" stock movement prediction "> stock movement prediction </a> </p> <a href="https://publications.waset.org/abstracts/118768/lexicon-based-sentiment-analysis-for-stock-movement-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118768.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">340</span> Financial Information Transparency on Investor Behavior in the Private Company in Dusit Area </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yosapon%20Kidsuntad">Yosapon Kidsuntad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this dissertation was to explore the relationship between financial transparency and investor behavior. In carrying out this inquiry, the researcher used a questionnaire was utilized as a tool to collect data. Statistics utilized in this research included frequency, percentage, mean, standard deviation, and multiple regression analysis. The results revealed that there are significant differences investor perceptions of the different dimensions of financial information transparency. These differences correspond to demographical variables with the exception of the educational level variable. It was also found that there are relationships between investor perceptions of the dimensions of financial information transparency and investor behavior in the private company in Dusit Area. Finally, the researcher also found that there are differences in investor behavior corresponding to different categories of investor experience. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20information%20transparency" title="financial information transparency">financial information transparency</a>, <a href="https://publications.waset.org/abstracts/search?q=investor%20behavior" title=" investor behavior"> investor behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=private%20company" title=" private company"> private company</a>, <a href="https://publications.waset.org/abstracts/search?q=Dusit%20Area" title=" Dusit Area "> Dusit Area </a> </p> <a href="https://publications.waset.org/abstracts/17784/financial-information-transparency-on-investor-behavior-in-the-private-company-in-dusit-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17784.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">330</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">339</span> Mask-Prompt-Rerank: An Unsupervised Method for Text Sentiment Transfer</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> Text sentiment transfer is an important branch of text style transfer. The goal is to generate text with another sentiment attribute based on a text with a specific sentiment attribute while maintaining the content and semantic information unrelated to sentiment unchanged in the process. There are currently two main challenges in this field: no parallel corpus and text attribute entanglement. In response to the above problems, this paper proposed a novel solution: Mask-Prompt-Rerank. Use the method of masking the sentiment words and then using prompt regeneration to transfer the sentence sentiment. Experiments on two sentiment benchmark datasets and one formality transfer benchmark dataset show that this approach makes the performance of small pre-trained language models comparable to that of the most advanced large models, while consuming two orders of magnitude less computing and memory. <p class="card-text"><strong>Keywords:</strong> <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=prompt" title=" prompt"> prompt</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20sentiment%20transfer" title=" text sentiment transfer"> text sentiment transfer</a> </p> <a href="https://publications.waset.org/abstracts/173904/mask-prompt-rerank-an-unsupervised-method-for-text-sentiment-transfer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173904.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">81</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">338</span> Fine-Grained Sentiment Analysis: Recent Progress</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jie%20Liu">Jie Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xudong%20Luo"> Xudong Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Pingping%20Lin"> Pingping Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yifan%20Fan"> Yifan Fan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, machine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future. <p class="card-text"><strong>Keywords:</strong> <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=fine-grained" title=" fine-grained"> fine-grained</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/140871/fine-grained-sentiment-analysis-recent-progress" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140871.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">337</span> An Online Adaptive Thresholding Method to Classify Google Trends Data Anomalies for Investor Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Duygu%20Dere">Duygu Dere</a>, <a href="https://publications.waset.org/abstracts/search?q=Mert%20Ergeneci"> Mert Ergeneci</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaan%20Gokcesu"> Kaan Gokcesu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Google Trends data has gained increasing popularity in the applications of behavioral finance, decision science and risk management. Because of Google’s wide range of use, the Trends statistics provide significant information about the investor sentiment and intention, which can be used as decisive factors for corporate and risk management fields. However, an anomaly, a significant increase or decrease, in a certain query cannot be detected by the state of the art applications of computation due to the random baseline noise of the Trends data, which is modelled as an Additive white Gaussian noise (AWGN). Since through time, the baseline noise power shows a gradual change an adaptive thresholding method is required to track and learn the baseline noise for a correct classification. To this end, we introduce an online method to classify meaningful deviations in Google Trends data. Through extensive experiments, we demonstrate that our method can successfully classify various anomalies for plenty of different data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20data%20processing" title="adaptive data processing">adaptive data processing</a>, <a href="https://publications.waset.org/abstracts/search?q=behavioral%20finance" title=" behavioral finance "> behavioral finance </a>, <a href="https://publications.waset.org/abstracts/search?q=convex%20optimization" title=" convex optimization"> convex optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20learning" title=" online learning"> online learning</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20minimum%20thresholding" title=" soft minimum thresholding"> soft minimum thresholding</a> </p> <a href="https://publications.waset.org/abstracts/92282/an-online-adaptive-thresholding-method-to-classify-google-trends-data-anomalies-for-investor-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92282.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">167</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">336</span> A Survey of the Applications of Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pingping%20Lin">Pingping Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Xudong%20Luo"> Xudong Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural language often conveys emotions of speakers. Therefore, sentiment analysis on what people say is prevalent in the field of natural language process and has great application value in many practical problems. Thus, to help people understand its application value, in this paper, we survey various applications of sentiment analysis, including the ones in online business and offline business as well as other types of its applications. In particular, we give some application examples in intelligent customer service systems in China. Besides, we compare the applications of sentiment analysis on Twitter, Weibo, Taobao and Facebook, and discuss some challenges. Finally, we point out the challenges faced in the applications of sentiment analysis and the work that is worth being studied in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=application" title="application">application</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=online%20comments" title=" online comments"> online comments</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/128022/a-survey-of-the-applications-of-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128022.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">263</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">335</span> Decoding WallStreetBets: The Impact of Daily Disagreements on Trading Volumes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Ghandehari">F. Ghandehari</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Lu"> H. Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20El-Jahel"> L. El-Jahel</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Jayasuriya"> D. Jayasuriya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Disagreement among investors is a fundamental aspect of financial markets, significantly influencing market dynamics. Measuring this disagreement has traditionally posed challenges, often relying on proxies like analyst forecast dispersion, which are limited by biases and infrequent updates. Recent movements in social media indicate that retail investors actively seek financial advice online and can influence the stock market. The evolution of the investing landscape, particularly the rise of social media as a hub for financial advice, provides an alternative avenue for real-time measurement of investor sentiment and disagreement. Platforms like Reddit offer rich, community-driven discussions that reflect genuine investor opinions. This research explores how social media empowers retail investors and the potential of leveraging textual analysis of social media content to capture daily fluctuations in investor disagreement. This study investigates the relationship between daily investor disagreement and trading volume, focusing on the role of social media platforms in shaping market dynamics, specifically using data from WallStreetBets (WSB) on Reddit. This paper uses data from 2020 to 2023 from WSB and analyses 4,896 firms with enough social media activity in WSB to define stock-day level disagreement measures. Consistent with traditional theories that disagreement induces trading volume, the results show significant evidence supporting this claim through different disagreement measures derived from WSB discussions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disagreement" title="disagreement">disagreement</a>, <a href="https://publications.waset.org/abstracts/search?q=retail%20investor" title=" retail investor"> retail investor</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20finance" title=" social finance"> social finance</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/188909/decoding-wallstreetbets-the-impact-of-daily-disagreements-on-trading-volumes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188909.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">39</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">334</span> Unsupervised Sentiment Analysis for Indonesian Political Message on Twitter </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Abdillah">Omar Abdillah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mirna%20Adriani"> Mirna Adriani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we perform new approach for analyzing public sentiment towards the presidential candidate in the 2014 Indonesian election that expressed in Twitter. In this study we propose such procedure for analyzing sentiment over Indonesian political message by understanding the behavior of Indonesian society in sending message on Twitter. We took different approach from previous works by utilizing punctuation mark and Indonesian sentiment lexicon that completed with the new procedure in determining sentiment towards the candidates. Our experiment shows the performance that yields up to 83.31% of average precision. In brief, this work makes two contributions: first, this work is the preliminary study of sentiment analysis in the domain of political message that has not been addressed yet before. Second, we propose such method to conduct sentiment analysis by creating decision making procedure in which it is in line with the characteristic of Indonesian message on Twitter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20sentiment%20analysis" title="unsupervised sentiment analysis">unsupervised sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=political%20message" title=" political message"> political message</a>, <a href="https://publications.waset.org/abstracts/search?q=lexicon%20based" title=" lexicon based"> lexicon based</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20behavior%20understanding" title=" user behavior understanding"> user behavior understanding</a> </p> <a href="https://publications.waset.org/abstracts/20304/unsupervised-sentiment-analysis-for-indonesian-political-message-on-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20304.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">480</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">333</span> A Survey of Sentiment Analysis Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pingping%20Lin">Pingping Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Xudong%20Luo"> Xudong Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yifan%20Fan"> Yifan Fan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20analysis" title="document analysis">document analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20sentiment%20analysis" title=" multimodal sentiment analysis"> multimodal sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/130107/a-survey-of-sentiment-analysis-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130107.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">164</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">332</span> Investor’s Psychology in Investment Decision Making in Context of Behavioural Finance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jhansi%20Rani%20Boda">Jhansi Rani Boda</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Sunitha"> G. Sunitha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Worldwide, the financial markets are influenced by several factors such as the changes in economic and political processes that occur in the country and the globe, information diffusion and approachability and so on. Yet, the foremost important factor is the investor’s reaction and perception. For an individual investor, decision-making process can be perceived as a continuous process that has significant impact of their psychology while making investment decisions. Behavioral finance relies on research of human and social recognition and emotional tolerance studies to identify and understand the investment decisions. This article aims to report the research of individual investor’s financial behavior in a historical perspective. This article uncovers the investor’s psychology in investment decision making focusing on the investor’s rationality with an explanation of psychological and emotional factors that affect investing. The results of the study are revealed by means of Graphical visualization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=behavioral%20finance" title="behavioral finance">behavioral finance</a>, <a href="https://publications.waset.org/abstracts/search?q=psychology" title=" psychology"> psychology</a>, <a href="https://publications.waset.org/abstracts/search?q=investor%E2%80%99s%20behavior" title=" investor’s behavior"> investor’s behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=psychological%20and%20emotional%20factors" title=" psychological and emotional factors"> psychological and emotional factors</a> </p> <a href="https://publications.waset.org/abstracts/77142/investors-psychology-in-investment-decision-making-in-context-of-behavioural-finance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77142.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">300</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">331</span> One-Class Support Vector Machine for Sentiment Analysis of Movie Review Documents </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chothmal">Chothmal</a>, <a href="https://publications.waset.org/abstracts/search?q=Basant%20Agarwal"> Basant Agarwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment analysis model to improve their products on the basis of users’ opinion. In this paper, we propose a new One-class Support Vector Machine (One-class SVM) based sentiment analysis model for movie review documents. In the proposed approach, we initially extract features from one class of documents, and further test the given documents with the one-class SVM model if a given new test document lies in the model or it is an outlier. Experimental results show the effectiveness of the proposed sentiment analysis model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection%20methods" title="feature selection methods">feature selection methods</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=NB" title=" NB"> NB</a>, <a href="https://publications.waset.org/abstracts/search?q=one-class%20SVM" title=" one-class SVM"> one-class SVM</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=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/37674/one-class-support-vector-machine-for-sentiment-analysis-of-movie-review-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37674.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">517</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">330</span> Sentiment Analysis: Comparative Analysis of Multilingual Sentiment and Opinion Classification Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sannikumar%20Patel">Sannikumar Patel</a>, <a href="https://publications.waset.org/abstracts/search?q=Brian%20Nolan"> Brian Nolan</a>, <a href="https://publications.waset.org/abstracts/search?q=Markus%20Hofmann"> Markus Hofmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Philip%20Owende"> Philip Owende</a>, <a href="https://publications.waset.org/abstracts/search?q=Kunjan%20Patel"> Kunjan Patel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross-language%20analysis" title="cross-language analysis">cross-language analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20translation" title=" machine translation"> machine translation</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/61790/sentiment-analysis-comparative-analysis-of-multilingual-sentiment-and-opinion-classification-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61790.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">713</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">329</span> Volatility Index, Fear Sentiment and Cross-Section of Stock Returns: Indian Evidence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pratap%20Chandra%20Pati">Pratap Chandra Pati</a>, <a href="https://publications.waset.org/abstracts/search?q=Prabina%20Rajib"> Prabina Rajib</a>, <a href="https://publications.waset.org/abstracts/search?q=Parama%20Barai"> Parama Barai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The traditional finance theory neglects the role of sentiment factor in asset pricing. However, the behavioral approach to asset-pricing based on noise trader model and limit to arbitrage includes investor sentiment as a priced risk factor in the assist pricing model. Investor sentiment affects stock more that are vulnerable to speculation, hard to value and risky to arbitrage. It includes small stocks, high volatility stocks, growth stocks, distressed stocks, young stocks and non-dividend-paying stocks. Since the introduction of Chicago Board Options Exchange (CBOE) volatility index (VIX) in 1993, it is used as a measure of future volatility in the stock market and also as a measure of investor sentiment. CBOE VIX index, in particular, is often referred to as the ‘investors’ fear gauge’ by public media and prior literature. The upward spikes in the volatility index are associated with bouts of market turmoil and uncertainty. High levels of the volatility index indicate fear, anxiety and pessimistic expectations of investors about the stock market. On the contrary, low levels of the volatility index reflect confident and optimistic attitude of investors. Based on the above discussions, we investigate whether market-wide fear levels measured volatility index is priced factor in the standard asset pricing model for the Indian stock market. First, we investigate the performance and validity of Fama and French three-factor model and Carhart four-factor model in the Indian stock market. Second, we explore whether India volatility index as a proxy for fearful market-based sentiment indicators affect the cross section of stock returns after controlling for well-established risk factors such as market excess return, size, book-to-market, and momentum. Asset pricing tests are performed using monthly data on CNX 500 index constituent stocks listed on the National stock exchange of India Limited (NSE) over the sample period that extends from January 2008 to March 2017. To examine whether India volatility index, as an indicator of fear sentiment, is a priced risk factor, changes in India VIX is included as an explanatory variable in the Fama-French three-factor model as well as Carhart four-factor model. For the empirical testing, we use three different sets of test portfolios used as the dependent variable in the in asset pricing regressions. The first portfolio set is the 4x4 sorts on the size and B/M ratio. The second portfolio set is the 4x4 sort on the size and sensitivity beta of change in IVIX. The third portfolio set is the 2x3x2 independent triple-sorting on size, B/M and sensitivity beta of change in IVIX. We find evidence that size, value and momentum factors continue to exist in Indian stock market. However, VIX index does not constitute a priced risk factor in the cross-section of returns. The inseparability of volatility and jump risk in the VIX is a possible explanation of the current findings in the study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=India%20VIX" title="India VIX">India VIX</a>, <a href="https://publications.waset.org/abstracts/search?q=Fama-French%20model" title=" Fama-French model"> Fama-French model</a>, <a href="https://publications.waset.org/abstracts/search?q=Carhart%20four-factor%20model" title=" Carhart four-factor model"> Carhart four-factor model</a>, <a href="https://publications.waset.org/abstracts/search?q=asset%20pricing" title=" asset pricing"> asset pricing</a> </p> <a href="https://publications.waset.org/abstracts/77209/volatility-index-fear-sentiment-and-cross-section-of-stock-returns-indian-evidence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77209.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">252</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">328</span> Sentiment Classification Using Enhanced Contextual Valence Shifters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vo%20Ngoc%20Phu">Vo Ngoc Phu</a>, <a href="https://publications.waset.org/abstracts/search?q=Phan%20Thi%20Tuoi"> Phan Thi Tuoi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We have explored different methods of improving the accuracy of sentiment classification. The sentiment orientation of a document can be positive (+), negative (-), or neutral (0). We combine five dictionaries from [2, 3, 4, 5, 6] into the new one with 21137 entries. The new dictionary has many verbs, adverbs, phrases and idioms, that are not in five ones before. The paper shows that our proposed method based on the combination of Term-Counting method and Enhanced Contextual Valence Shifters method has improved the accuracy of sentiment classification. The combined method has accuracy 68.984% on the testing dataset, and 69.224% on the training dataset. All of these methods are implemented to classify the reviews based on our new dictionary and the Internet Movie data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20classification" title="sentiment classification">sentiment classification</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20orientation" title=" sentiment orientation"> sentiment orientation</a>, <a href="https://publications.waset.org/abstracts/search?q=valence%20shifters" title=" valence shifters"> valence shifters</a>, <a href="https://publications.waset.org/abstracts/search?q=contextual" title=" contextual"> contextual</a>, <a href="https://publications.waset.org/abstracts/search?q=valence%20shifters" title=" valence shifters"> valence shifters</a>, <a href="https://publications.waset.org/abstracts/search?q=term%20counting" title=" term counting"> term counting</a> </p> <a href="https://publications.waset.org/abstracts/11410/sentiment-classification-using-enhanced-contextual-valence-shifters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11410.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">504</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">327</span> An Enhanced Support Vector Machine Based Approach for Sentiment Classification of Arabic Tweets of Different Dialects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gehad%20S.%20Kaseb">Gehad S. Kaseb</a>, <a href="https://publications.waset.org/abstracts/search?q=Mona%20F.%20Ahmed"> Mona F. Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Arabic Sentiment Analysis (SA) is one of the most common research fields with many open areas. Few studies apply SA to Arabic dialects. This paper proposes different pre-processing steps and a modified methodology to improve the accuracy using normal Support Vector Machine (SVM) classification. The paper works on two datasets, Arabic Sentiment Tweets Dataset (ASTD) and Extended Arabic Tweets Sentiment Dataset (Extended-AATSD), which are publicly available for academic use. The results show that the classification accuracy approaches 86%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic" title="Arabic">Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</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=tweets" title=" tweets"> tweets</a> </p> <a href="https://publications.waset.org/abstracts/138144/an-enhanced-support-vector-machine-based-approach-for-sentiment-classification-of-arabic-tweets-of-different-dialects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138144.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">149</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">326</span> From Text to Data: Sentiment Analysis of Presidential Election Political Forums</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergio%20V%20Davalos">Sergio V Davalos</a>, <a href="https://publications.waset.org/abstracts/search?q=Alison%20L.%20Watkins"> Alison L. Watkins</a> </p> <p class="card-text"><strong>Abstract:</strong></p> User generated content (UGC) such as website post has data associated with it: time of the post, gender, location, type of device, and number of words. The text entered in user generated content (UGC) can provide a valuable dimension for analysis. In this research, each user post is treated as a collection of terms (words). In addition to the number of words per post, the frequency of each term is determined by post and by the sum of occurrences in all posts. This research focuses on one specific aspect of UGC: sentiment. Sentiment analysis (SA) was applied to the content (user posts) of two sets of political forums related to the US presidential elections for 2012 and 2016. Sentiment analysis results in deriving data from the text. This enables the subsequent application of data analytic methods. The SASA (SAIL/SAI Sentiment Analyzer) model was used for sentiment analysis. The application of SASA resulted with a sentiment score for each post. Based on the sentiment scores for the posts there are significant differences between the content and sentiment of the two sets for the 2012 and 2016 presidential election forums. In the 2012 forums, 38% of the forums started with positive sentiment and 16% with negative sentiment. In the 2016 forums, 29% started with positive sentiment and 15% with negative sentiment. There also were changes in sentiment over time. For both elections as the election got closer, the cumulative sentiment score became negative. The candidate who won each election was in the more posts than the losing candidates. In the case of Trump, there were more negative posts than Clinton’s highest number of posts which were positive. KNIME topic modeling was used to derive topics from the posts. There were also changes in topics and keyword emphasis over time. Initially, the political parties were the most referenced and as the election got closer the emphasis changed to the candidates. The performance of the SASA method proved to predict sentiment better than four other methods in Sentibench. The research resulted in deriving sentiment data from text. In combination with other data, the sentiment data provided insight and discovery about user sentiment in the US presidential elections for 2012 and 2016. <p class="card-text"><strong>Keywords:</strong> <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>, <a href="https://publications.waset.org/abstracts/search?q=user%20generated%20content" title=" user generated content"> user generated content</a>, <a href="https://publications.waset.org/abstracts/search?q=US%20presidential%20elections" title=" US presidential elections"> US presidential elections</a> </p> <a href="https://publications.waset.org/abstracts/86945/from-text-to-data-sentiment-analysis-of-presidential-election-political-forums" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86945.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">192</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">325</span> Saudi Twitter Corpus for Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adel%20Assiri">Adel Assiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Emam"> Ahmed Emam</a>, <a href="https://publications.waset.org/abstracts/search?q=Hmood%20Al-Dossari"> Hmood Al-Dossari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis (SA) has received growing attention in Arabic language research. However, few studies have yet to directly apply SA to Arabic due to lack of a publicly available dataset for this language. This paper partially bridges this gap due to its focus on one of the Arabic dialects which is the Saudi dialect. This paper presents annotated data set of 4700 for Saudi dialect sentiment analysis with (K= 0.807). Our next work is to extend this corpus and creation a large-scale lexicon for Saudi dialect from the corpus. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic" title="Arabic">Arabic</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=Twitter" title=" Twitter"> Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=annotation" title=" annotation"> annotation</a> </p> <a href="https://publications.waset.org/abstracts/44819/saudi-twitter-corpus-for-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44819.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">630</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">324</span> The Use of AI to Measure Gross National Happiness</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Riona%20Dighe">Riona Dighe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</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=gross%20national%20happiness" title=" gross national happiness"> gross national happiness</a> </p> <a href="https://publications.waset.org/abstracts/160021/the-use-of-ai-to-measure-gross-national-happiness" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160021.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">323</span> 1/Sigma Term Weighting Scheme for Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hanan%20Alshaher">Hanan Alshaher</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinsheng%20Xu"> Jinsheng Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify if the entropy reflects the discriminating power of terms, we report a comparison of entropy values for different term weighting schemes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=1%2Fsigma" title="1/sigma">1/sigma</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=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=term%20weighting%20scheme" title=" term weighting scheme"> term weighting scheme</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a> </p> <a href="https://publications.waset.org/abstracts/134006/1sigma-term-weighting-scheme-for-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134006.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">204</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">322</span> Collision Theory Based Sentiment Detection Using Discourse Analysis in Hadoop</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anuta%20Mukherjee">Anuta Mukherjee</a>, <a href="https://publications.waset.org/abstracts/search?q=Saswati%20Mukherjee"> Saswati Mukherjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data is growing everyday. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing a large increase in the growth of data. It is a rich source especially for sentiment detection or mining since people often express honest opinion through tweets. However, although sentiment analysis is a well-researched topic in text, this analysis using Twitter data poses additional challenges since these are unstructured data with abbreviations and without a strict grammatical correctness. We have employed collision theory to achieve sentiment analysis in Twitter data. We have also incorporated discourse analysis in the collision theory based model to detect accurate sentiment from tweets. We have also used the retweet field to assign weights to certain tweets and obtained the overall weightage of a topic provided in the form of a query. Hadoop has been exploited for speed. Our experiments show effective results. <p class="card-text"><strong>Keywords:</strong> <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=twitter" title=" twitter"> twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=collision%20theory" title=" collision theory"> collision theory</a>, <a href="https://publications.waset.org/abstracts/search?q=discourse%20analysis" title=" discourse analysis"> discourse analysis</a> </p> <a href="https://publications.waset.org/abstracts/36495/collision-theory-based-sentiment-detection-using-discourse-analysis-in-hadoop" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36495.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">535</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">321</span> Performance Evaluation of an Ontology-Based Arabic Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salima%20Behdenna">Salima Behdenna</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatiha%20Barigou"> Fatiha Barigou</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghalem%20Belalem"> Ghalem Belalem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the quick increase in the volume of Arabic opinions posted on various social media, Arabic sentiment analysis has become one of the most important areas of research. Compared to English, there is very little works on Arabic sentiment analysis, in particular aspect-based sentiment analysis (ABSA). In ABSA, aspect extraction is the most important task. In this paper, we propose a semantic aspect-based sentiment analysis approach for standard Arabic reviews to extract explicit aspect terms and identify the polarity of the extracted aspects. The proposed approach was evaluated using HAAD datasets. Experiments showed that the proposed approach achieved a good level of performance compared with baseline results. The F-measure was improved by 19% for the aspect term extraction tasks and 55% aspect term polarity task. <p class="card-text"><strong>Keywords:</strong> <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=opinion%20mining" title=" opinion mining"> opinion mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic" title=" Arabic"> Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=aspect%20level" title=" aspect level"> aspect level</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion" title=" opinion"> opinion</a>, <a href="https://publications.waset.org/abstracts/search?q=polarity" title=" polarity"> polarity</a> </p> <a href="https://publications.waset.org/abstracts/135240/performance-evaluation-of-an-ontology-based-arabic-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135240.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">163</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">320</span> Investor Beware - Significance of Investor Conduct under the Fair and Equitable Treatment Standard</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Damayanti%20Sen">Damayanti Sen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Fair and Equitable Treatment standard has emerged as a core tenet of a formulated legal structure aimed at encouraging investment through the granting of a secure and stable environment for the investor in the Host State. As an absolute, non-contingent standard, it constitutes an independent and reliable system for the protection of the investor and is frequently invoked and applied in investor-state dispute settlement under bilateral and multilateral investment treaties. Thus far, the standard has been examined principally as a measure for determining the responsibility of host countries towards investors and investments. The conduct of investor in applying the Fair and Equitable Treatment Standard is relatively unexplored. Such an assessment may be necessary in light of the development of new defenses to demands of host governments to confine the application of the standard in order to ensure a proper balance between the protection of investors and the inherent right of a State to regulate economic conduct within its borders. This paper explores the implications of including considerations of investor conduct in the determination of whether an act of the host country’s administrative and/or judicial authorities has breached the fair and equitable treatment principle. The need for such defenses are of special concern for governments of developing countries, whose limited resources can affect their ability to provide an effective evaluation of the nature of the proposed investment, and, subsequently, to ensure that the expected benefits are realized. On the basis of conceptual analysis, and emerging international judicial and arbitral case law, this paper suggests that investor duties such as, the avoidance of unconscionable conduct, the reasonable assessment of investment risk in the host country, and a duty to operate an investment reasonably are leading to a new limit upon the fair and equitable treatment standard- one that can be succinctly captured in the phrase “Caveat Investor”. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BITs" title="BITs">BITs</a>, <a href="https://publications.waset.org/abstracts/search?q=FET%20Standard" title=" FET Standard"> FET Standard</a>, <a href="https://publications.waset.org/abstracts/search?q=investor%20behavior" title=" investor behavior"> investor behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=arbitral%20case%20law" title=" arbitral case law"> arbitral case law</a> </p> <a href="https://publications.waset.org/abstracts/25203/investor-beware-significance-of-investor-conduct-under-the-fair-and-equitable-treatment-standard" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25203.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">313</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">319</span> Linguistic Features for Sentence Difficulty Prediction in Aspect-Based Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adrian-Gabriel%20Chifu">Adrian-Gabriel Chifu</a>, <a href="https://publications.waset.org/abstracts/search?q=Sebastien%20Fournier"> Sebastien Fournier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that aims to extract and analyze these subjective elements from text, and it can be applied at different levels of granularity, such as document, paragraph, sentence, or aspect. Aspect-based sentiment analysis is a well-studied topic with many available data sets and models. However, there is no clear definition of what makes a sentence difficult for aspect-based sentiment analysis. In this paper, we explore this question by conducting an experiment with three data sets: ”Laptops”, ”Restaurants”, and ”MTSC” (Multi-Target-dependent Sentiment Classification), and a merged version of these three datasets. We study the impact of domain diversity and syntactic diversity on difficulty. We use a combination of classifiers to identify the most difficult sentences and analyze their characteristics. We employ two ways of defining sentence difficulty. The first one is binary and labels a sentence as difficult if the classifiers fail to correctly predict the sentiment polarity. The second one is a six-level scale based on how many of the top five best-performing classifiers can correctly predict the sentiment polarity. We also define 9 linguistic features that, combined, aim at estimating the difficulty at sentence level. <p class="card-text"><strong>Keywords:</strong> <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=difficulty" title=" difficulty"> difficulty</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</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/177853/linguistic-features-for-sentence-difficulty-prediction-in-aspect-based-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177853.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">89</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">318</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" 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