Enhanced Twitter Sentiment Classification Using Contextual Information
The rise in popularity and ubiquity of Twitter has made sentiment analysis of tweets an important and well-covered area of research. However, the 140 character limit imposed on tweets makes it hard to use standard linguistic methods for sentiment classification. On the other han...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Association for Computational Linguistics
2015
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| Online Access: | http://hdl.handle.net/1721.1/98527 https://orcid.org/0000-0002-2564-8909 https://orcid.org/0000-0002-4333-7194 |
| _version_ | 1826204012162056192 |
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| author | Vosoughi, Soroush Zhou, Helen L. Roy, Deb K |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Vosoughi, Soroush Zhou, Helen L. Roy, Deb K |
| author_sort | Vosoughi, Soroush |
| collection | MIT |
| description | The rise in popularity and ubiquity of Twitter has made sentiment analysis of
tweets an important and well-covered area of research. However, the 140 character limit imposed on tweets makes it hard to use standard linguistic methods for sentiment classification. On the other hand, what tweets lack in structure they make up with sheer volume and rich metadata. This metadata includes geolocation, temporal and author information. We hypothesize that sentiment is dependent on all these contextual factors. Different locations, times and authors have different emotional valences. In this paper, we explored this hypothesis by utilizing distant supervision to collect millions of labelled tweets from different locations, times and authors. We used this data to analyse the variation of tweet sentiments across different authors, times and locations. Once we explored and understood the relationship between these variables and sentiment, we used a Bayesian approach to combine these variables with more standard linguistic features such as n-grams to create a Twitter sentiment classifier. This combined classifier outperforms the purely linguistic classifier, showing that integrating the rich contextual information available on Twitter into sentiment classification is a promising direction of research. |
| first_indexed | 2024-09-23T12:47:26Z |
| format | Article |
| id | mit-1721.1/98527 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:47:26Z |
| publishDate | 2015 |
| publisher | Association for Computational Linguistics |
| record_format | dspace |
| spelling | mit-1721.1/985272022-09-28T10:01:38Z Enhanced Twitter Sentiment Classification Using Contextual Information Vosoughi, Soroush Zhou, Helen L. Roy, Deb K Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Media Laboratory Program in Media Arts and Sciences (Massachusetts Institute of Technology) Vosoughi, Soroush Zhou, Helen L. Roy, Deb K. The rise in popularity and ubiquity of Twitter has made sentiment analysis of tweets an important and well-covered area of research. However, the 140 character limit imposed on tweets makes it hard to use standard linguistic methods for sentiment classification. On the other hand, what tweets lack in structure they make up with sheer volume and rich metadata. This metadata includes geolocation, temporal and author information. We hypothesize that sentiment is dependent on all these contextual factors. Different locations, times and authors have different emotional valences. In this paper, we explored this hypothesis by utilizing distant supervision to collect millions of labelled tweets from different locations, times and authors. We used this data to analyse the variation of tweet sentiments across different authors, times and locations. Once we explored and understood the relationship between these variables and sentiment, we used a Bayesian approach to combine these variables with more standard linguistic features such as n-grams to create a Twitter sentiment classifier. This combined classifier outperforms the purely linguistic classifier, showing that integrating the rich contextual information available on Twitter into sentiment classification is a promising direction of research. Twitter (Firm) 2015-09-16T12:13:13Z 2015-09-16T12:13:13Z 2015-09 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/98527 Vosoughi, Soroush, Helen Zhou, and Deb Roy. "Enhanced Twitter Sentiment Classification Using Contextual Information." 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP) 6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) (September 2015). https://orcid.org/0000-0002-2564-8909 https://orcid.org/0000-0002-4333-7194 en_US https://aclweb.org/anthology/W/W15/W15-2904.pdf Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP) 6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computational Linguistics Vosoughi |
| spellingShingle | Vosoughi, Soroush Zhou, Helen L. Roy, Deb K Enhanced Twitter Sentiment Classification Using Contextual Information |
| title | Enhanced Twitter Sentiment Classification Using Contextual Information |
| title_full | Enhanced Twitter Sentiment Classification Using Contextual Information |
| title_fullStr | Enhanced Twitter Sentiment Classification Using Contextual Information |
| title_full_unstemmed | Enhanced Twitter Sentiment Classification Using Contextual Information |
| title_short | Enhanced Twitter Sentiment Classification Using Contextual Information |
| title_sort | enhanced twitter sentiment classification using contextual information |
| url | http://hdl.handle.net/1721.1/98527 https://orcid.org/0000-0002-2564-8909 https://orcid.org/0000-0002-4333-7194 |
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