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Reading the Markets: Forecasting Public Opinion of Political Candidates by News Analysis

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Reading the Markets: Forecasting Public Opinion of Political Candidates by News Analysis Reading the Markets: Forecasting Public Opinion of Political Candidates by News Analysis Kevin Lerman Ari Gilder and Mark Dredze Fernando Pereira Dept. of Computer Science Dept. of CIS Google, Inc. Columbia University University of Pennsylvania 1600 Amphitheatre Parkway New York, NY USA Philadelphia, PA USA Mountain View, CA USA [email protected] [email protected] [email protected] [email protected] Abstract between the frequency of a candidate’s name in the news and electoral success (Veronis,´ 2007). Media reporting shapes public opinion This work forecasts day-to-day changes in pub- which can in turn influence events, partic- lic perception of political candidates from daily ularly in political elections, in which can- news. Measuring daily public perception with didates both respond to and shape public polls is problematic since they are conducted by a perception of their campaigns. We use variety of organizations at different intervals and computational linguistics to automatically are not easily comparable. Instead, we rely on predict the impact of news on public per- daily measurements from prediction markets. ception of political candidates. Our sys- We present a computational system that uses tem uses daily newspaper articles to pre- both external linguistic information and internal dict shifts in public opinion as reflected market indicators to forecast public opinion mea- in prediction markets. We discuss various sured by prediction markets. We use features from types of features designed for this prob- syntactic dependency parses of the news and a lem. The news system improves market user-defined set of market entities. Successive prediction over baseline market systems. news days are compared to determine the novel component of each day’s news resulting in fea- 1 Introduction tures for a machine learning system. A combina- The mass media can affect world events by sway- tion system uses this information as well as predic- ing public opinion, officials and decision mak- tions from internal market forces to model predic- ers. Financial investors who evaluate the economic tion markets better than several baselines. Results performance of a company can be swayed by pos- show that news articles can be mined to predict itive and negative perceptions about the company changes in public opinion. in the media, directly impacting its economic po- Opinion forecasting differs from that of opin- sition. The same is true of politics, where a can- ion analysis, such as extracting opinions, evaluat- didate’s performance is impacted by media influ- ing sentiment, and extracting predictions (Kim and enced public perception. Computational linguis- Hovy, 2007). Contrary to these tasks, our system tics can discover such signals in the news. For receives objective news, not subjective opinions, example, Devitt and Ahmad (2007) gave a com- and learns what events will impact public opinion. putable metric of polarity in financial news text For example, “oil prices rose” is a fact but will consistent with human judgments. Koppel and likely shape opinions. This work analyzes news Shtrimberg (2004) used a daily news analysis to (cause) to predict future opinions (effect). This af- predict financial market performance, though pre- fects the structure of our task: we consider a time- dictions could not be used for future investment series setting since we must use past data to predict decisions. Recently, a study conducted of the 2007 future opinions, rather than analyzing opinions in French presidential election showed a correlation batch across the whole dataset. We begin with an introduction to prediction market provides a daily average price, which indi- markets. Several methods for new feature extrac- cates the overall market sentiment for a candidate tion are explored as well as market history base- on a given day. The goal of the prediction system lines. Systems are evaluated on prediction markets is to predict the price direction for the next day (up from the 2004 US Presidential election. We close or down) given all available information up to the with a discussion of related and future work. current day: previous days’ market pricing/volume information and the morning news. Market his- 2 Prediction Markets tory represents information internal to the market: Prediction markets, such as TradeSports and the if an investor has no knowledge of external events, Iowa Electronic Markets1, provide a setting sim- what is the most likely direction for the market? ilar to financial markets wherein shares represent This information can capture general trends and not companies or commodities, but an outcome volatility of the market. The daily news is the ex- of a sporting, financial or political event. For ex- ternal information that influences the market. This ample, during the 2004 US Presidential election, provides information, independent of any internal one could purchase a share of “George W. Bush to market effects to which investors will respond. A win the 2004 US Presidential election” or “John learning system for each information source is de- Kerry to win the 2004 US Presidential election.” veloped and combined to explain market behavior. A pay-out of $1 is awarded to winning sharehold- The following sections describe these systems. ers at market’s end, e.g. Bush wins the election. In the interim, price fluctuations driven by supply 3 External Information: News and demand indicate the perception of the event’s likelihood, which indicates public opinion of an Changes in market price are likely responses to event. Several studies show the accuracy of predic- current events reported in the news. Investors read tion markets in predicting future events (Wolfers the morning paper and act based on perceptions of and Zitzewitz, 2004; Servan-Schreiber et al., 2004; events. Can a system with access to this same in- Pennock et al., 2000), such as the success of up- formation make good investment decisions? coming movies (Jank and Foutz, 2007), political Our system operates in an iterative (online) stock markets (Forsythe et al., 1999) and sports fashion. On each day (round) the news for that betting markets (Williams, 1999). day is used to construct a new instance. A logistic Market investors rely on daily news reports to regression classifier is trained on all previous days dictate investment actions. If something positive and the resulting classifier predicts the price move- happens for Bush (e.g. Saddam Hussein is cap- ment of the new instance. The system either profits tured), Bush will appear more likely to win, so or loses money according to this prediction. It then demand increases for “Bush to win” shares, and receives the actual price movement and labels the the price rises. Likewise, if something negative instance accordingly (up or down). This setting for Bush occurs (e.g. casualties in Iraq increase), is straightforward; the difficulty is in choosing a people will think he is less likely to win, sell their good feature representation for the classifier. We shares, and the price drops. Therefore, predic- now explore several representation techniques. tion markets can be seen as rapid response indi- cators of public mood concerning political candi- 3.1 Bag-of-Words Features dates. Market-internal factors, such as general in- The prediction task can be treated as a document vestor mood and market history, also affect price. classification problem, where the document is the For instance, a positive news story for a candidate day’s news and the label is the direction of the may have less impact if investors dislike the can- market. Document classification systems typically didate. Explaining market behavior requires mod- rely on bag-of-words features, where each feature eling news information external to the market and indicates the number of occurrences of a word in internal trends to the market. the document. The news for a given day is rep- This work uses the 2004 US Presidential elec- resented by a normalized unit length vector of tion markets from Iowa Electronic Markets. Each counts, excluding common stop words and fea- 1 www.tradesports.com, www.biz.uiowa.edu/iem/ tures that occur fewer than 20 times in our corpus. 3.2 News Focus Features are conjunctions of the word and the entity. For ex- Simple bag-of-words features may not capture rel- ample, the sentence “Bush is facing another scan- evant news information. Public opinion is influ- dal” produces the feature “bush-scandal” instead 2 enced by new events – a change in focus. The day of just “scandal.” Context disambiguation comes after a debate, most papers may declare Bush the at a high cost: about 70% of all sentences do not winner, yielding a rise in the price of a “Bush to contain any predefined entities and about 7% con- win” share. However, while the debate may be tain more than one entity. These likely relevant discussed for several days after the event, public sentences are unfortunately discarded, although opinion of Bush will probably not continue to rise future work could reduce the number of discarded on old news. Changes in public opinion should sentences using coreference resolution. reflect changes in daily news coverage. Instead of 3.4 Dependency Features constructing features for a single day, they can rep- While entity features are helpful they cannot pro- resent differences between two days of news cov- cess multiple entity sentences, nearly a quarter of erage, i.e. the novelty of the coverage. Given the the entity sentences. These sentences may be the counts of feature i on day t as ct, where feature i i most helpful since they indicate entity interactions. may be the unigram “scandal,” and the set of fea- Consider the following three example sentences: tures on day t as Ct, the fraction of news focus for t t ci each feature is fi = |Ct| .
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