An Experiment in Integrating Sentiment Features for Tech Stock Prediction in Twitter

Shu Chang · Quang-Thuy Ha · Nigel Collier

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Sentiment Analysis and Opinion Mining

Abstract

Economic analysis indicates a relationship between consumer sentiment and stock price movements. In this study we harness features from Twitter messages to capture public mood related to four Tech companies for predicting the daily up and down price movements of these companies’ NASDAQ stocks. We propose a novel model combining features namely positive and negative sentiment, consumer confidence in the product with respect to ‘bullish’ or ‘bearish’ lexicon and three previous stock market movement days. The features are employed in a Decision Tree classifier using cross-fold validation to yield accuracies of 82.93%,80.49%, 75.61% and 75.00% in predicting the daily up and down changes of Apple (AAPL), Google (GOOG), Microsoft (MSFT) and Amazon (AMZN) stocks respectively in a 41 market day sample.

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References

1965The Behavior of Stock-Market Prices 2011Twitter mood predicts the stock market 2003Constructivist and Ecological Rationality in Economics 2010Widespread Worry and the Stock Market 2011Happiness Is Assortative in Online Social Networks 2009Using Prediction Markets and Twitter to Predict a Swine Flu Pandemic 2011Identifying and Following Expert Investors in Stock Microblogs

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