Web Sentiment Analysis for Revealing Public Opinions, Trends and Making Good Financial Decisions

Bissattini, Cristian; Christodoulou, Kostis

doi:10.2139/ssrn.2309375

Cited by 5 publications

(7 citation statements)

References 7 publications

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“…Bollen et al (2011) improved the accuracy of DJIA predictions using public mood dimensions extracted from Twitter. Furthermore, it has been shown that social network sentiment can help in predicting stock market movements (Oh and Sheng, 2011;Makrehchi et al, 2013;Bissattini and Christodoulou, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The sentiment analysis of sentences allows measuring what people think and relating it to different events. Along this line, several studies analyzed the relationship between social network sentiment and the stock market (Oh and Sheng, 2011;Rao and Srivastava, 2012;Bissattini and Christodoulou, 2013;Oliveira et al, 2013), and found that sentiment can help in predicting stock market variables such as trading volume, returns, or market movements. Nevertheless, in order to obtain a better prediction, it is necessary to take many more variables into account, such as market risk (Ribeiro-Soriano and Urbano, 2010), or Tobin's Q (Piñeiro-Chousa et al, 2016), as well as other information from social networks (e.g., message volume, certain user profile characteristics) (Piñeiro-Chousa et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing Microblogging Activity and Stock Market Behavior through Artificial Neural Networks

Juan¹,

Ángeles²,

Pérez-Pico³

et al. 2020

JBAFP

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This paper attempts to analyze the relationship between social network activity (message sentiment) and stock market (trading volume and risk premium). We used Artificial Neural Networks to analyze 87,511 stock-related microblogging messages related to S&P500 Index posted between October 2009 and October 2014. The results obtained suggest that there is a direct relationship between trading volume and negative sentiment, and between risk premium and negative sentiment. The paper concludes with several directions for future research.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing Microblogging Activity and Stock Market Behavior through Artificial Neural Networks

Juan¹,

Ángeles²,

Pérez-Pico³

et al. 2020

JBAFP

View full text Add to dashboard Cite

show abstract

“…A vast majority of researchers have analyzed the relationship between stock market variables and one microblogging variable such as posting volume (Tumarkin & Whitelaw, 2001;Wysocki, 1998) or message sentiment (Bissattini & Christodoulou, 2013). However, some authors have analyzed two or more microblogging variables together and their influence in stock market.…”

Section: H2: Social Media Sentiment Influences Stock Marketsmentioning

confidence: 99%

“…Nowadays, social media is a part of daily life, which requires the understanding of how social media influences society with a potential to change consumers’ or investors’ behavior, resulting in consequences that will undoubtedly reach markets at all levels. Some variables from social networks and their users can result helpful in predicting the market performance (Bissattini & Chistodoulou, ; Bollen, Mao, & Zeng, ). In this sense, one of the most used sources is StockTwits.com, which provides accurate data in order to predict market performance (Oh & Sheng, ).…”

mentioning

confidence: 99%

Influence of Social Media over the Stock Market

Chousa

Vizcaíno-González

Pérez-Pico

2016

Psychology and Marketing

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This research analyzes investors’ activity through social media and these media's influence over the Chicago Board Options Exchange Market Volatility Index (VIX) using a logit model and a fuzzy‐set qualitative comparative analysis (fsQCA). The logit results show that social media sentiment influences stock markets. Meanwhile, the fsQCA results show that the investors’ profile is important for explaining how social media influence the stock market. Particularly, holding period combined with experience in technical investors contributes to avoiding a raise in market risk, whereas for nontechnical investors message sentiment and experience form the combination that contributes to avoid a raise in market risk.

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“…As major companies are increasingly coming to realize, these consumer voices can wield enormous influence in shaping the opinions of other consumer and, ultimately, their brand loyalties, their purchase decisions, and their own brand advocacy. Companies can respond to the consumer insights they generate through social media monitoring and analysis by modifying their marketing message, brand positing, product development, and other activities accordingly [3][4]…”

Section: Introductionmentioning

confidence: 99%

Web Sentiment Analysis for Scoring Positive or Negative Words using Tweeter Data

PappuRajan¹,

Victor²

2014

IJCA

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As people are free to say their opinions on anything using various social networking sites like Twitter, Facebook, Discussion forums, and blogs. Particularly Microblogging and text messaging have emerged and become dominated tool over the web. Twitter messages (tweets) is often used to share opinions and sentiments about the surrounding world. The availability of social content generated on sites such as Twitter creates new opportunities to study public opinion about the entity. This analysis we took twitter data for sentiment classification. The Sentiment analysis is done on a per-Tweet basis. The words in each Tweet are compared with those in other Tweets that have been previously labeled as "positive", or "negative". After looking at these words, the algorithm then judges whether the text in the Tweet is positive or negative based on the likelihood for each possibility. The overall objective of this paper is to determine the sentiment of the text, whether it is positive or negative, which is extended to strength of polarity also this approach is used to obtain the significant features and to analyzing the overall sentiment for each object by computing the weighted average for all the sentiments in the textual data.

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Web Sentiment Analysis for Revealing Public Opinions, Trends and Making Good Financial Decisions

Cited by 5 publications

References 7 publications

Analyzing Microblogging Activity and Stock Market Behavior through Artificial Neural Networks

Analyzing Microblogging Activity and Stock Market Behavior through Artificial Neural Networks

Influence of Social Media over the Stock Market

Web Sentiment Analysis for Scoring Positive or Negative Words using Tweeter Data

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