{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/twitter-mood-predicts-the-stock-market","title":"Twitter mood predicts the stock market","arxiv_id":"1010.3003","date":"2010-10-14","proceeding":null,"authors":["Johan Bollen","Huina Mao","Xiao-jun Zeng"],"abstract":"Behavioral economics tells us that emotions can profoundly affect individual\nbehavior and decision-making. Does this also apply to societies at large, i.e.,\ncan societies experience mood states that affect their collective decision\nmaking? By extension is the public mood correlated or even predictive of\neconomic indicators? Here we investigate whether measurements of collective\nmood states derived from large-scale Twitter feeds are correlated to the value\nof the Dow Jones Industrial Average (DJIA) over time. We analyze the text\ncontent of daily Twitter feeds by two mood tracking tools, namely OpinionFinder\nthat measures positive vs. negative mood and Google-Profile of Mood States\n(GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital,\nKind, and Happy). We cross-validate the resulting mood time series by comparing\ntheir ability to detect the public's response to the presidential election and\nThanksgiving day in 2008. A Granger causality analysis and a Self-Organizing\nFuzzy Neural Network are then used to investigate the hypothesis that public\nmood states, as measured by the OpinionFinder and GPOMS mood time series, are\npredictive of changes in DJIA closing values. Our results indicate that the\naccuracy of DJIA predictions can be significantly improved by the inclusion of\nspecific public mood dimensions but not others. We find an accuracy of 87.6% in\npredicting the daily up and down changes in the closing values of the DJIA and\na reduction of the Mean Average Percentage Error by more than 6%.","url_abs":"http://arxiv.org/abs/1010.3003v1","url_pdf":"http://arxiv.org/pdf/1010.3003v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"twitter-mood-predicts-the-stock-market","repo_url":"https://github.com/beekpower/we-feel-extract","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"twitter-mood-predicts-the-stock-market","repo_url":"https://github.com/peanutshawny/lstm-stock-predictor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"twitter-mood-predicts-the-stock-market","repo_url":"https://github.com/williamwparker/Trade-Bot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"stock-market-prediction","task_name":"Stock Market Prediction"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1010.3003","atlas_url":"https://app.syntology.ai/?focus=1010.3003","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}