Making objective decisions from subjective data: Detecting irony in customer reviews
The research described in this work focuses on identifying key components for the task of irony detection. By means of analyzing a set of customer reviews, which are considered ironic both in social and mass media, we try to find hints about how to deal with this task from a computational point of v...
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| Veröffentlicht in: | Decision Support Systems 2012-11, Vol.53 (4), p.754-760 |
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| creator | Reyes, Antonio Rosso, Paolo |
| description | The research described in this work focuses on identifying key components for the task of irony detection. By means of analyzing a set of customer reviews, which are considered ironic both in social and mass media, we try to find hints about how to deal with this task from a computational point of view. Our objective is to gather a set of discriminating elements to represent irony, in particular, the kind of irony expressed in such reviews. To this end, we built a freely available data set with ironic reviews collected from Amazon. Such reviews were posted on the basis of an online viral effect; i.e. contents that trigger a chain reaction in people. The findings were assessed employing three classifiers. Initial results are largely positive, and provide valuable insights into the subjective issues of language facing tasks such as sentiment analysis, opinion mining and decision making. |
| doi_str_mv | 10.1016/j.dss.2012.05.027 |
| format | Article |
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| source | Elsevier ScienceDirect Journals |
| subjects | Classifiers Computation Data mining Decision making Decision support systems Decisions Electronic commerce Irony detection Natural language processing On-line systems Online Product reviews Sentiment analysis Social networks Studies Tasks Web text analysis |
| title | Making objective decisions from subjective data: Detecting irony in customer reviews |
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