Exploiting Topic based Twitter Sentiment for Stock Prediction

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Exploiting Topic based Twitter Sentiment for Stock Prediction

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by Jianfeng Si , Arjun Mukherjee , Bing Liu , Qing Li , Huayi Li , Xiaotie Deng

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13 - 2 self

BibTeX

@MISC{Si_exploitingtopic,
    author = {Jianfeng Si and Arjun Mukherjee and Bing Liu and Qing Li and Huayi Li and Xiaotie Deng},
    title = {Exploiting Topic based Twitter Sentiment for Stock Prediction},
    year = {}
}

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Abstract

This paper proposes a technique to leverage topic based sentiments from Twitter to help predict the stock market. We first utilize a continuous Dirichlet Process Mixture model to learn the daily topic set. Then, for each topic we derive its sentiment according to its opinion words distribution to build a sentiment time series. We then regress the stock index and the Twitter sentiment time series to predict the market. Experiments on real-life S&P100 Index show that our approach is effective and performs better than existing state-of-the-art non-topic based methods. 1

Keyphrases

twitter sentiment stock prediction daily topic set state-of-the-art non-topic continuous dirichlet process mixture model stock market sentiment time series stock index opinion word distribution real-life p100 index twitter sentiment time series

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