Twitter Sentiment Mining: A Multi Domain Analysis
Microblogging such as Twitter provides a rich source of information about products, personalities, and trends, etc. We proposed a simple methodology for analyzing sentiment of users in Twitter. First, we automatically collected Twitter corpus in positive and negative tweets. Second, we built a simpl...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
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CPS
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/34303 |
| _version_ | 1848754186684989440 |
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| author | Shahheidari, S. Dong, Hai Bin Daud, M.N.R. |
| author2 | Leonard Barolli |
| author_facet | Leonard Barolli Shahheidari, S. Dong, Hai Bin Daud, M.N.R. |
| author_sort | Shahheidari, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Microblogging such as Twitter provides a rich source of information about products, personalities, and trends, etc. We proposed a simple methodology for analyzing sentiment of users in Twitter. First, we automatically collected Twitter corpus in positive and negative tweets. Second, we built a simple sentiment classifier by utilizing the Naive Bayes model to determine the positive and negative sentiment of a tweet. Third, we tested the classifier against a collection of users’ opinions from five interesting domains of Twitter, i.e., news, finance, job, movies, and sport. The experimental results show that it is feasible to use Twitter corpus alone to classify new tweet for a certain domain applications. |
| first_indexed | 2025-11-14T08:36:25Z |
| format | Conference Paper |
| id | curtin-20.500.11937-34303 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:36:25Z |
| publishDate | 2013 |
| publisher | CPS |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-343032023-02-13T08:01:34Z Twitter Sentiment Mining: A Multi Domain Analysis Shahheidari, S. Dong, Hai Bin Daud, M.N.R. Leonard Barolli Fatos Xhafa Hsing-Chung Chen Antonio F. Skarmeta Gómez Farookh Hussain social media text mining classifier Opinion mining sentiment analysis Microblogging such as Twitter provides a rich source of information about products, personalities, and trends, etc. We proposed a simple methodology for analyzing sentiment of users in Twitter. First, we automatically collected Twitter corpus in positive and negative tweets. Second, we built a simple sentiment classifier by utilizing the Naive Bayes model to determine the positive and negative sentiment of a tweet. Third, we tested the classifier against a collection of users’ opinions from five interesting domains of Twitter, i.e., news, finance, job, movies, and sport. The experimental results show that it is feasible to use Twitter corpus alone to classify new tweet for a certain domain applications. 2013 Conference Paper http://hdl.handle.net/20.500.11937/34303 10.1109/CISIS.2013.31 CPS fulltext |
| spellingShingle | social media text mining classifier Opinion mining sentiment analysis Shahheidari, S. Dong, Hai Bin Daud, M.N.R. Twitter Sentiment Mining: A Multi Domain Analysis |
| title | Twitter Sentiment Mining: A Multi Domain Analysis |
| title_full | Twitter Sentiment Mining: A Multi Domain Analysis |
| title_fullStr | Twitter Sentiment Mining: A Multi Domain Analysis |
| title_full_unstemmed | Twitter Sentiment Mining: A Multi Domain Analysis |
| title_short | Twitter Sentiment Mining: A Multi Domain Analysis |
| title_sort | twitter sentiment mining: a multi domain analysis |
| topic | social media text mining classifier Opinion mining sentiment analysis |
| url | http://hdl.handle.net/20.500.11937/34303 |