Finding and Quantifying Temporal-Aware Contradiction in Reviews

Abstract : Opinions (reviews) on web resources (e.g., courses, movies), generated by users, become increasingly exploited in text analysis tasks, the detection of contradictory opinions being one of them. This paper focuses on the quantification of sentiment-based contradictions around specific aspects in reviews. However, it is necessary to study the contradictions with respect to the temporal dimension of reviews (their sessions). In general, for web resources such as online courses (e.g. coursera or edX), reviews are often generated during the course sessions. Between sessions, users stop reviewing courses, and there are chances that courses will be updated. So, in order to avoid the confusion of contradictory reviews coming from two or more different sessions, the reviews related to a given resource should be firstly grouped according to their corresponding session. Secondly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Thirdly, the polarity of each review segment containing an aspect is estimated. Then, only resources containing these aspects with opposite polarities are considered. Finally, the contradiction intensity is estimated based on the joint dispersion of polarities and ratings of the reviews containing aspects. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. The results confirm the effectiveness of our approach to find and quantify contradiction intensity.
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Communication dans un congrès
Won-Kyung Sung, Hanmin Jung, Shuo Xu, Krisana Chinnasarn, Kazutoshi Sumiya, Jeonghoon Lee, Zhicheng Dou, Grace Hui Yang, Young-Guk Ha, Seungbock Lee. 13th Asia Information Retrieval Societies Conference (AIRS 2017), Nov 2017, Jeju Island, South Korea. Springer, Lecture Notes in Computer Science, 10648, pp.167-180, 2017, Information Retrieval Technology. 〈10.1007/978-3-319-70145-5_13〉
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https://hal.archives-ouvertes.fr/hal-01904434
Contributeur : Ismail Badache <>
Soumis le : lundi 5 novembre 2018 - 18:50:59
Dernière modification le : mercredi 13 mars 2019 - 16:09:04
Document(s) archivé(s) le : mercredi 6 février 2019 - 13:36:14

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Ismail Badache, Sébastien Fournier, Adrian-Gabriel Chifu. Finding and Quantifying Temporal-Aware Contradiction in Reviews. Won-Kyung Sung, Hanmin Jung, Shuo Xu, Krisana Chinnasarn, Kazutoshi Sumiya, Jeonghoon Lee, Zhicheng Dou, Grace Hui Yang, Young-Guk Ha, Seungbock Lee. 13th Asia Information Retrieval Societies Conference (AIRS 2017), Nov 2017, Jeju Island, South Korea. Springer, Lecture Notes in Computer Science, 10648, pp.167-180, 2017, Information Retrieval Technology. 〈10.1007/978-3-319-70145-5_13〉. 〈hal-01904434〉

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