Twitter mood predicts the stock market
http://arxiv.org/abs/1010.3003
Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e., can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate 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. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%.
Younggue Bae , Hongchul Lee, A sentiment analysis of audiences on twitter: who is the positive or negative audience of popular twitterers?, Proceedings of the 5th international conference on Convergence and hybrid information technology, September 22-24, 2011, Daejeon, Korea
http://dl.acm.org/citation.cfm?id=1944594&CFID=76892041&CFTOKEN=90820401
Automated identification of diverse sentiment types can be beneficial for many NLP systems such as review summarization and public media analysis. In some of these systems there is an option of assigning a sentiment value to a single sentence or a very short text.In this paper we propose a supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service. By utilizing 50 Twitter tags and 15 smileys as sentiment labels, this framework avoids the need for labor intensive manual annotation, allowing identification and classification of diverse sentiment types of short texts. We evaluate the contribution of different feature types for sentiment classification and show that our framework successfully identifies sentiment types of untagged sentences. The quality of the sentiment identification was also confirmed by human judges. We also explore dependencies and overlap between different sentiment types represented by smileys and Twitter hashtags.
http://dl.acm.org/citation.cfm?id=1944594&CFID=76892041&CFTOKEN=90820401
Effective sentiment stream analysis with self-augmenting training and demand-driven projection
http://dl.acm.org/citation.cfm?id=2009981&CFID=76892041&CFTOKEN=90820401
How do we analyze sentiments over a set of opinionated Twitter messages? This issue has been widely studied in recent years, with a prominent approach being based on the application of classification techniques. Basically, messages are classified according to the implicit attitude of the writer with respect to a query term. A major concern, however, is that Twitter (and other media channels) follows the data stream model, and thus the classifier must operate with limited resources, including labeled data for training classification models. This imposes serious challenges for current classification techniques, since they need to be constantly fed with fresh training messages, in order to track sentiment drift and to provide up-to-date sentiment analysis.
We propose solutions to this problem. The heart of our approach is a training augmentation procedure which takes as input a small training seed, and then it automatically incorporates new relevant messages to the training data. Classification models are produced on-the-fly using association rules, which are kept up-to-date in an incremental fashion, so that at any given time the model properly reflects the sentiments in the event being analyzed. In order to track sentiment drift, training messages are projected on a demand driven basis, according to the content of the message being classified. Projecting the training data offers a series of advantages, including the ability to quickly detect trending information emerging in the stream. We performed the analysis of major events in 2010, and we show that the prediction performance remains about the same, or even increases, as the stream passes and new training messages are acquired. This result holds for different languages, even in cases where sentiment distribution changes over time, or in cases where the initial training seed is rather small. We derive lower-bounds for prediction performance, and we show that our approach is extremely effective under diverse learning scenarios, providing gains that range from 7% to 58%.
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