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Understanding Twitter Sentiment for Investing Decisions


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This research presentation provides an overview of ongoing research into data mining twitter to determine sentiment, insight and knowledge found in tweets. An overview will be provided of the research project a review of my research questions and hypothesis’ as well as a review of the theoretical basis for this research.

This research has been completed. You can view the outcome of this research at

Also, check out my website and service for investing in the markets using Twitter Sentiment at

The research proposed herein will be my Doctoral Dissertation Topic.

Understanding Twitter Sentiment for Investing Decisions

  1. 1. Analysis of Twitter Messages forSentiment and Insight for use in StockMarket Decision MakingEric D. BrownDakota State University
  2. 2. Introduction• Agenda • Background • Literature Review • Research Summary & Model • Research Questions & Hypotheses • Contributions • Research Methods • Preliminary Results • Discussion & Challenges • Conclusions & Future Work
  3. 3. Background• Sentiment has long been an underlying factor in the investing world • Consumer Confidence Index • Investors Intelligence Sentiment Index • “Market Sentiment”• Rather than waiting days, months or weeks, can the ‘sentiment of now’ be used to improve trading performance and investing decisions?• Can Twitter be used to determine the ‘sentiment of now’?
  4. 4. BackgroundThe thoughts driving this research are:• Can analysis of publicly available Twitter Messages provide insight for decision making for investing?• Do Twitter messages (and their subsequent sentiment) have any effect on movement in the stock market?• Can Twitter messages be mined and analyzed to predict movements in the stock market?• Does a Twitter user’s reputation have an effect on how people perceive and use their shared investing ideas?
  5. 5. Literature Review• Wysoki (1998) – Strong positive correlation between volume of messages posted on message boards overnight and next day’s trading volume and stock returns• Tumarkin and Whitelaw (2001) – concluded that there are no predictive capabilities found within message board activity• Antweiler and Frank (2004) – Used sentiment analysis to show strong positive correlation between message board posts and next day trading volume and volatility. Showed minor correlation between message board posts and next day price activity.
  6. 6. Literature Review• Gu, et al (2006) – Found that aggregation of individual recommendations on stock message boards have no predictive power on future stock returns• Das and Chen (2007) – Using sentiment analysis of messages on message boards, found no correlation between sentiment and individual stock price movement but did find positive correlation of the aggregate sentiment of a set of aggregate stocks and movement in the stock market• Zhang (2009) – Studied the reputation of a message board poster and showed that a ‘better’ reputation were shared more and had a higher effect on sentiment
  7. 7. Literature Review• Bollen, Mao & Zeng (2010) – Using sentiment analysis, determines the ‘mood’ of the twitter universe and then predicts the next day movement of the Dow Jones Industrial Average – with an 87.6% accuracy. • This model is being used by a Hedge Fund to actively trade. The first month of trading showed profitability• Sprenger and Welpe (2010) – Focused on the S&P 100 stocks and the sentiment of those stocks. Showed that sentiment of the company on Twitter closely follows market movements. Also shows positive correlation between trading volume and message volume on Twitter for that company. • Research laid the groundwork for
  8. 8. Literature Review• Vincent & Armstrong (2010) – Undertook a research project to understand how Twitter ‘buzz’ and measure the ‘change of context’ within Twitter messages. They called this change of context the ‘breaking point’ - when messages turn from Bullish to Bearish (and vice versa). • Using these ‘breaking point’, a profitable automated trading system was developed to trade the Forex market.• Saavedra, Hagerty, Uzzi (2011) – Studied a proprietary trading firm to determine if ideas shared through instant messaging platforms lead to increased performance (measured in terms of profitability).
  9. 9. Literature Review• Additional research in Sentiment Analysis of Twitter: • Bifet & Frank, 2010 - Sentiment knowledge discovery in twitter streaming data • Pak & Paroubek, 2010 - Twitter as a Corpus for Sentiment analysis and Opinion Mining. • Romero, Meeder, & Klienberg, 2010 - Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter • Castillo, Mendoza & Poblete, 2010 - Information credibility on twitter. • Diakopoulos & Shamma, 2010 - Characterizing debate performance via aggregated twitter sentiment.
  10. 10. Research Summary• The goal of this research is a more thorough understanding of Twitter users and their sharing of investing ideas and how those ideas can or should be used in investing decisions• If Twitter messages do convey some form of sentiment which is correlated to activity in the stock market, can this sentiment be used in a predictive manner?• Can this large distributed network of users be ‘tapped’ to build a decision support system for the generation of investing ideas?
  11. 11. Research Summary• This research will attempt to: • Study how individuals use Twitter to share and consume knowledge in support of their investing decisions • Determine whether correlation exists between the sentiment of a Tweet and movement in the stock market • Determine whether there are times of day (or days of the week) that provide more ‘weight’ toward sentiment • Understand how a user’s reputation might affect the sentiment of a company or sector
  12. 12. Research Model User ReputationTwitter Sentiment Analysis Social For Stocks and Sectors Analysis of Twitter Users Stock & H1a, H1b, H1c Sector H4a, H4b Analysis Information Content of Sentiment Twitter Weighting H2a, H2b Messages within Sectors Correlations Predictive Nature with Stock of Shared Tweets Market Day / H3 Movement How users might Time use Twitter for Analysis Decision Support in investing
  13. 13. Research Questions & Hypotheses• RQ-1: Using a given sector of the stock market, does the sentiment for that sector match the weighted sentiment for the stocks within that sector? How well does the sentiment predict price and volume movement? • H1a: The sentiment of a sector will match the overall averaged sentiment of all stocks within the sector. • H1b: The sentiment of a sector can be used to predict the movement of all stocks in that sector. • H1c: The sentiment of a sector or stock on any given day will provide a prediction for the next day’s movement in that stock or sector
  14. 14. Research Questions & Hypotheses• RQ-2: Are there specific stocks within a given sector that supply the majority of the sentiment for that sector? If so, do these stocks supply sentiment in correlation to the weighting of those stocks in the sector? • H2a: The sentiment of a stock within a sector will affect the sentiment of the sector based on the relative weighting of that stock within the sector. • H2b: The stocks that provide the most weight toward the sentiment of a sector are also the stocks with the highest number of mentions on Twitter.
  15. 15. Research Questions & Hypotheses• RQ-3: Are there times of the day or days of the week that provide a more accurate and informative sentiment for a stock or sector? • H3: Messages sent during non-market hours (i.e., evenings and weekends) will have the most effect on sentiment for the following day
  16. 16. Research Questions & Hypotheses• RQ-4: Are there specific users that provide more ‘weight’ to a sentiment of a stock or sector based on the users’ reputation? Do retweets by these users (or of these users’ tweets) provide more weight for the sentiment of a stock or sector? • H4a: The number of followers of a Twitter user determines the effect that users’ tweets will have on sentiment for a stock or sector. • H4b: A message sent or retweeted by a user with a large number of Twitter followers will provide more weight toward the sentiment of a stock or sector.
  17. 17. Contributions• Extends the body of knowledge for Sentiment Analysis of Twitter for decision support in the investing domain• Extends the body of knowledge in regards to the information content of Twitter messages and how users might use that knowledge for decision support• Gaining a better understanding of how a user’s reputation effects the sharing of their information• Building a Text Corpus that can be used in future sentiment analysis research for twitter messages
  18. 18. Research Method Stock Twitter Data Market Collection Data Price & Sentiment Social Volume Analysis Analysis Analysis Positive Correlation of sentiment and Reputation of message volume with price/volume Twitter userUnderstanding of predictive capabilities of Twitter Sentiment and the affect of user reputation investing decision support
  19. 19. Research Method• Data Collection • Using Twitter API to collect tweets (tweet, sender, date, time) • Tweets referencing companies and sectors are collected and stored in a MySQL database for future study • Using the nomenclature made popular by StockTwits ( Example: The stock symbol for Apple is AAPL. Users following the StockTwits nomenclature add a “$” to the symbol – “$AAPL”. • describes their purpose as a place to: • …share ideas, market insights and trades on stocks, futures and the market in general *. • Using Yahoo Finance data feed to gather Stock Market data (price and volume) • Provides historical data
  20. 20. Research Method• Sentiment Analysis• Using a Naïve Bayesian text classification algorithm to determine sentiment of collected Tweets • Naïve Bayesian is being used for simplicity but also because many researchers have pointed out very minor differences between it and other sentiment analysis methods • A subset of the data collected will be manually assigned ‘sentiment’ to build the necessary training dataset• Using the R Programming language combined with Python and the WEKA Data Mining Platform, a text classification algorithms will be implemented to determine sentiment• For each tweet, the overall score is calculated and assigned. • Ideally, tweets will fall into +1 (Bullish), 0 (Neutral), -1 (Bearish) buckets. • Currently, tweet sentiments are summed and added without being normalized
  21. 21. Research Method• Price and Volume Analysis • Using regression and other analysis techniques, the movements in price and volume will be compared with sentiment of stocks and sectors to determine if any predictive capabilities exist between sentiment, tweet message volume, price movement and volume. • The Autoregressive integrated moving average (ARIMA) and Granger Causality Analysis techniques are being considered for use in this project for modeling predictive behavior. Other appropriate statistical techniques may also be considered
  22. 22. Research Method• Social Analysis • Using social graphs and analysis of twitter users, determine whether a tweet sent by a user with more followers provides more ‘weight’ to a sentiment of the stocks mentioned in the tweet. • Using the concept of ‘retweets’, determine how far a user’s tweet travels via ‘retweets’, can any form of reputation or ‘trust’ of that user be determined?
  23. 23. Preliminary Results• A short study was conducted in May 2011 (May 2 to May 11) to determine viability of data collection and sentiment analysis • 2 Stock Market Sectors chosen to collect data: • Energy (XLE) – consists of 41 companies • Consumer Staples (XLP) – consists of 41 companies • Using the Twitter API and the collection prototype, a ten day run was initiated • 13,000 tweets collected for XLE, XLP and 82 companies comprising the sectors• Basic Naïve Bayesian approach to determining sentiment using the R programming language• For a quick test, Hu and Liu’s (2004) polarity dataset used to determine / assign sentiment
  24. 24. Preliminary Results• The XLE ETF saw about an seven-point drop from a high of $80.80 on May 2, 2011 to a low of $73.70 on May 11, 2011. Courtesy of
  25. 25. Preliminary Results• XLE Average Sentiment: 0.115• 1=Bullish / Positive; 0=Neutral; -1=Bearish / Negative• 253 tweets captured for XLE
  26. 26. Preliminary Results• XLE Price Movement compared with Sentiment
  27. 27. Preliminary Results Volume Data via Yahoo Finance
  28. 28. Preliminary ResultsTop 5 Holdings of the XLE ETF and their Sentiment Company Symbol % of ETF Price Change ($) Average Sentiment Exxon / XOM 19.02 % -7.16 -0.034 Chevron / CVX 15.03 % -6.99 0.113 Schlumberger / SLB 7.12 % -7.28 0.413Conocophillips / COP 5.17 % -7.36 0.258 Occidental / OXY 4.32 % -13.31 0.173
  29. 29. Preliminary Results• The XLP ETF saw a slight upward move from $31.12 on May 2, 2011 to $31.24 on May 11, 2011 . Courtesy of
  30. 30. Preliminary Results• XLP Average Sentiment: 0.5• 1=Bullish / Positive; 0=Neutral; -1=Bearish / Negative• 52 tweets captured for XLP
  31. 31. Preliminary Results• XLP Price Movement compared with Daily Sentiment
  32. 32. Preliminary Results Volume Data via Yahoo Finance
  33. 33. Preliminary ResultsTop 5 Holdings of the XLP ETF and their Sentiment Company Symbol % of ETF Price Change ($) Average SentimentProctor & Gamble / PG 14.69 % 1.58 0.418 Philip Morris / PM 9.60 % -1.48 0.134 Wal Mart / WMT 8.00 % 1.40 0.171 Coca Cola / KO 7.48 % 0.41 0.671 Kraft Foods / KFT 5.04 % 1.40 0.254
  34. 34. Preliminary Results• Social Analysis • One of the users that sent a Tweet during this test was a twitter user named “gtotoy”. • He has 6,502 twitter followers • Sent 28,869 tweets • One Tweet by gtotoy for was retweeted by 10 separate twitter users • According to, gtotoy’s reach over the last 50 tweets (as of Oct 26 2011 @ 12:22PM): • 13,161 people • 145,125 Impressions • Will gtotoy’s tweets (and subsequent retweets) provide more ‘weight’ for a stock or sector’s sentiment?
  35. 35. Social Graph for “gtotoy”
  36. 36. Preliminary Results - Discussion• There’s not enough data gathered over the 10 day period to begin to answer any research questions.• Purpose of the preliminary test was to validate the research method and approach – data can be collected, scored and analyzed.• Data collection has been ongoing since May 1
  37. 37. Conclusions & Future Work• There are some challenges to this research: • Building the training dataset will be key • Building a corpus of investing and trading ‘words’ for positive, neutral and bearish opinions • Continued Access to Twitter API • Will Twitter “Spam” have an affect on sentiment? • Can sarcasm be detected? If not, how does it effect sentiment?
  38. 38. Conclusions & Future Work• Based on the initial analysis presented, there appears to be an interesting study to be done to determine if Twitter Sentiment has predictive capabilities• Next Steps: • Dissertation Approval • Continue data collection • Determine Modeling and Predictive Analysis Approaches • Complete Analysis & Research • Write up
  39. 39. Questions? Thank you