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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2013
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| Online Access: | https://www.redalyc.org/articulo.oa?id=61527437010 |
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| _version_ | 1866815606454484992 |
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| author | Narendra K. Gupta |
| author_facet | Narendra K. Gupta |
| contents | Extracting Phrases Describing Problems with Products and Services from Twitter Messages Narendra K. Gupta Computación Social media text classification information extraction Social media contain many types of information useful to businesses. In this paper we discuss a trigger-target based approach to extract descriptions of problems from Twitter data. It is important to note that the descriptions of problems are factual statements as opposed to subjective opinions about products/services. We first identify the problem tweets i.e. the tweets containing descriptions of problems. We then extract the phrases that describe the problem. In our approach such descriptions are extracted as a combination of trigger and target phrases. Triggers are mostly domain independent verb phrases and are identified by using hand crafted lexical and syntactic patterns. Targets on the other hand are domain specific noun phrases syntactically related to the triggers. We frame the problem of finding target phrase corresponding to a trigger phrase as a ranking problem and show the results of experiments with maximum entropy classifiers and voted perceptrons. Both approaches outperform the rule based approach reported before. 2013 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61527437010 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.2 Vol.17 |
| format | Artículo científico |
| id | redalyc_61527437010 |
| language | en |
| publishDate | 2013 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Extracting Phrases Describing Problems with Products and Services from Twitter Messages Narendra K. Gupta Computación Social media text classification information extraction Extracting Phrases Describing Problems with Products and Services from Twitter Messages Narendra K. Gupta Computación Social media text classification information extraction Social media contain many types of information useful to businesses. In this paper we discuss a trigger-target based approach to extract descriptions of problems from Twitter data. It is important to note that the descriptions of problems are factual statements as opposed to subjective opinions about products/services. We first identify the problem tweets i.e. the tweets containing descriptions of problems. We then extract the phrases that describe the problem. In our approach such descriptions are extracted as a combination of trigger and target phrases. Triggers are mostly domain independent verb phrases and are identified by using hand crafted lexical and syntactic patterns. Targets on the other hand are domain specific noun phrases syntactically related to the triggers. We frame the problem of finding target phrase corresponding to a trigger phrase as a ranking problem and show the results of experiments with maximum entropy classifiers and voted perceptrons. Both approaches outperform the rule based approach reported before. 2013 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61527437010 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.2 Vol.17 |
| title | Extracting Phrases Describing Problems with Products and Services from Twitter Messages |
| topic | Computación Social media text classification information extraction |
| url | https://www.redalyc.org/articulo.oa?id=61527437010 |