Exploiting Topic based Twitter Sentiment for Stock Prediction
Article Structure
Abstract
This paper proposes a technique to leverage topic based sentiments from Twitter to help predict the stock market.
Introduction
Social media websites such as Twitter, Facebook, etc., have become ubiquitous platforms for social networking and content sharing.
Related Work 2.1 Market Prediction and Social Media
Stock market prediction has attracted a great deal of attention in the past.
Topics
Gibbs sampling
Appears in 3 sentences as: Gibbs Sampler (1) Gibbs sampling (2)
In Exploiting Topic based Twitter Sentiment for Stock Prediction
- We use collapsed Gibbs sampling (Bishop, 2006) for model inference.Page 2, “Related Work 2.1 Market Prediction and Social Media”
- Only non-opinion words in tweets are used for Gibbs sampling .Page 3, “Related Work 2.1 Market Prediction and Social Media”
- 1 The actual topic priors for topic links are governed by the four cases of the Gibbs Sampler .Page 4, “Related Work 2.1 Market Prediction and Social Media”
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LDA
Appears in 3 sentences as: LDA (3)
In Exploiting Topic based Twitter Sentiment for Stock Prediction
- One of the basic and most widely used models is Latent Dirichlet Allocation ( LDA ) (Blei et al., 2003).Page 2, “Related Work 2.1 Market Prediction and Social Media”
- LDA can learn a predefined number of topics and has been widely applied in its extended forms in sentiment analysis and many other tasks (Mei et al., 2007; Branavan et al., 2008; Lin and He, 2009; Zhao et al., 2010; Wang et al., 2010; Brody and Elhadad, 2010; Jo and Oh, 2011; Moghaddam and Ester, 2011; Sauper et al., 2011; Mukherjee and Liu, 2012; He et al., 2012).Page 2, “Related Work 2.1 Market Prediction and Social Media”
- The Dirichlet Processes Mixture (DPM) model is a nonparametric extension of LDA (Teh et al., 2006), which can estimate the number of topics inherent in the data itself.Page 2, “Related Work 2.1 Market Prediction and Social Media”
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sentiment analysis
Appears in 3 sentences as: sentiment analysis (3)
In Exploiting Topic based Twitter Sentiment for Stock Prediction
- (2011) introduced a hybrid approach for stock sentiment analysis based on companies’ news articles.Page 2, “Related Work 2.1 Market Prediction and Social Media”
- LDA can learn a predefined number of topics and has been widely applied in its extended forms in sentiment analysis and many other tasks (Mei et al., 2007; Branavan et al., 2008; Lin and He, 2009; Zhao et al., 2010; Wang et al., 2010; Brody and Elhadad, 2010; Jo and Oh, 2011; Moghaddam and Ester, 2011; Sauper et al., 2011; Mukherjee and Liu, 2012; He et al., 2012).Page 2, “Related Work 2.1 Market Prediction and Social Media”
- In this work, we employ topic based sentiment analysis using DPM on Twitter posts (or tweets).Page 2, “Related Work 2.1 Market Prediction and Social Media”
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social media
Appears in 3 sentences as: Social media (1) social media (2)
In Exploiting Topic based Twitter Sentiment for Stock Prediction
- Social media websites such as Twitter, Facebook, etc., have become ubiquitous platforms for social networking and content sharing.Page 1, “Introduction”
- Some recent researches suggest that news and social media such as blogs, micro-blogs, etc., can be analyzed to extract public sentiments to help predict the market (La-vrenko et al., 2000; Schumaker and Chen, 2009).Page 1, “Related Work 2.1 Market Prediction and Social Media”
- The topics mostly focus on hot keywords like: news, stocknews, earning, report, which stimulate active discussions on the social media platform.Page 4, “Related Work 2.1 Market Prediction and Social Media”
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