Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”
TL;DR: This paper found that emotional tweet percentage significantly negatively correlated with Dow Jones, NASDAQ and S&P 500, but displayed a significant positive correlation to VIX, and that just checking on twitter for emotional outbursts of any kind gives a predictor of how the stock market will be doing the next day.
About: This article is published in Procedia - Social and Behavioral Sciences.The article was published on 2011-01-01 and is currently open access. It has received 573 citations till now. The article focuses on the topics: Stock market.
Citations
More filters
TL;DR: This work investigates whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time and indicates that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others.
4,453 citations
Cites background from "Predicting Stock Market Indicators ..."
...In this paper we investigate whether public sentiment, as expressed in large-scale collections of daily Twitter posts, can be used to predict the stock market....
[...]
Book•
01 Jun 2015TL;DR: Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes as discussed by the authors, which offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis.
Abstract: Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.
587 citations
01 Sep 2016
TL;DR: This comprehensive introduction to sentiment analysis takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions.
531 citations
TL;DR: This paper shows an evaluation of the effectiveness of the sentiment analysis in the stock prediction task via a large scale experiment and a novel method for predicting stock price movement using the sentiment from social media.
Abstract: A novel method for predicting stock price movement was presentedTopics and sentiments of them were extracted from social media as the featureTwo methods were proposed to capture the topic-sentiment featureIntegration of the sentiments was investigated via a large scale experimentOur model outperformed other methods in the average accuracy of 18 stocks The goal of this research is to build a model to predict stock price movement using the sentiment from social media Unlike previous approaches where the overall moods or sentiments are considered, the sentiments of the specific topics of the company are incorporated into the stock prediction model Topics and related sentiments are automatically extracted from the texts in a message board by using our proposed method as well as existing topic models In addition, this paper shows an evaluation of the effectiveness of the sentiment analysis in the stock prediction task via a large scale experiment Comparing the accuracy average over 18 stocks in one year transaction, our method achieved 207% better performance than the model using historical prices only Furthermore, when comparing the methods only for the stocks that are difficult to predict, our method achieved 983% better accuracy than historical price method, and 303% better than human sentiment method
393 citations
13 Nov 2011
TL;DR: In this article, the authors use the recent Irish General======Election as a case study for investigating the potential to model political sentiment through mining of social media, and they find that social analytics using both volume-based measures and sentiment analysis are predictive.
Abstract: The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an
electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General
Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using
supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using
both volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during
an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content.
377 citations
Cites methods or result from "Predicting Stock Market Indicators ..."
...This work is echoed by preliminary work from Zhang et al. who also focus on emotive concepts, in this case “hope” and “fear”, and correlate with a number of market indicators (Zhang et al., 2010)....
[...]
...This is followed in Section 3 by a description of our methodology....
[...]
References
More filters
Proceedings Article•
16 May 2010TL;DR: An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.
Abstract: Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user's influence on others — a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.
3,041 citations
"Predicting Stock Market Indicators ..." refers background in this paper
...Cha et al. (2010) compared three different measures of influence − indegree, retweets and user mentions....
[...]
12 Aug 2007
TL;DR: It is found that people use microblogging to talk about their daily activities and to seek or share information and the user intentions associated at a community level are analyzed to show how users with similar intentions connect with each other.
Abstract: Microblogging is a new form of communication in which users can describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. Twitter, a popular microblogging tool has seen a lot of growth since it launched in October, 2006. In this paper, we present our observations of the microblogging phenomena by studying the topological and geographical properties of Twitter's social network. We find that people use microblogging to talk about their daily activities and to seek or share information. Finally, we analyze the user intentions associated at a community level and show how users with similar intentions connect with each other.
3,025 citations
"Predicting Stock Market Indicators ..." refers background in this paper
...By examining the follower network, Java et al. (2007) found that there is a great variety in users’ intentions....
[...]
Proceedings Article•
16 May 2010TL;DR: It is found that the mere number of messages mentioning a party reflects the election result, and joint mentions of two parties are in line with real world political ties and coalitions.
Abstract: Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician Our results show that Twitter is indeed used extensively for political deliberation We find that the mere number of messages mentioning a party reflects the election result Moreover, joint mentions of two parties are in line with real world political ties and coalitions An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research
2,718 citations
"Predicting Stock Market Indicators ..." refers background in this paper
...In their study, Tumasjan et al. (2010) analyzed Twitter messages mentioning parties and politicians prior to the German federal election 2009 and found that the mere number of tweets reflects voter preferences and comes close to traditional election polls....
[...]
04 Feb 2010
TL;DR: Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank, which is proposed to measure the influence of users in Twitter.
Abstract: This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.
1,974 citations
05 Jan 2010
TL;DR: This paper examines the practice of retweeting as a way by which participants can be "in a conversation" and highlights how authorship, attribution, and communicative fidelity are negotiated in diverse ways.
Abstract: Twitter - a microblogging service that enables users to post messages ("tweets") of up to 140 characters - supports a variety of communicative practices; participants use Twitter to converse with individuals, groups, and the public at large, so when conversations emerge, they are often experienced by broader audiences than just the interlocutors. This paper examines the practice of retweeting as a way by which participants can be "in a conversation." While retweeting has become a convention inside Twitter, participants retweet using different styles and for diverse reasons. We highlight how authorship, attribution, and communicative fidelity are negotiated in diverse ways. Using a series of case studies and empirical data, this paper maps out retweeting as a conversational practice.
1,953 citations