Using Multi-View Learning to Improve Detection of Investor Sentiments on Twitter
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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2014
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| _version_ | 1876434861512720384 |
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| author | Zvi Ben-Ami |
| author_facet | Zvi Ben-Ami |
| contents | Using Multi-View Learning to Improve Detection of Investor Sentiments on Twitter Zvi Ben-Ami Ronen Feldman Binyamin Rosenfeld Computación multi multi multi multi multi 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. 2014 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61532067006 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.18 |
| format | Artículo científico |
| id | redalyc_61532067006 |
| institution | Redalyc |
| language | en |
| publishDate | 2014 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Using Multi-View Learning to Improve Detection of Investor Sentiments on Twitter Zvi Ben-Ami Computación multi multi multi multi multi Using Multi-View Learning to Improve Detection of Investor Sentiments on Twitter Zvi Ben-Ami Ronen Feldman Binyamin Rosenfeld Computación multi multi multi multi multi 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. 2014 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61532067006 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.18 |
| title | Using Multi-View Learning to Improve Detection of Investor Sentiments on Twitter |
| topic | Computación multi multi multi multi multi |
| url | https://www.redalyc.org/articulo.oa?id=61532067006 |