Using Multi-View Learning to Improve Detection of Investor Sentiments on Twitter

Zvi Ben-Ami¹, Ronen Feldman², and Binyamin Rosenfeld²

¹ The Hebrew University, School of Business Administration, Jerusalem, Israel. zvi.benami@mail.huji.ac.il

² Digital Trowel, New York, USA. ronen.feldman@mail.huji.ac.il, grurgrur@gmail.com.

Abstract

Stock-related messages on social media have several interesting properties regarding the sentiment analysis (SA) task. On the one hand, the analysis is particularly challenging, because of frequent typos, bad grammar, and idiosyncratic expressions specific to the domain and media. On the other hand, stock-related messages primarily refer to the state of specific entities – companies and their stocks, at specific times (of sending). This state is an objective property and even has a measurable numeric characteristic, namely, the stock price. Given a large dataset of twitter messages, we can create two separate "views" on the dataset by analyzing text of messages and external properties separately. With this, we can expand the coverage of generic SA tools and learn new sentiment expressions. In this paper, we experiment with this learning method, comparing several types of general SA tools and sets of external properties. The method is shown to produce significant improvement in accuracy.

Keywords: Sentiment analysis, sentiment expression mining, unsupervised learning, multi-view learning, investors' sentiments, social media.

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Acknowledgements

This work is supported by the Israel Ministry of Science and Technology Center of Knowledge in Machine Learning and Artificial Intelligence and the Israel Ministry of Defense.

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