Geierhos, Michaela; Bäumer, Frederik Simon; Schulze, Sabine; Stuß, Valentina
Dokumenttyp:
Sammelbandbeitrag / Paper in Collective Volume
Titel:
Filtering Reviews by Random Individual Error
Herausgeber Sammlung:
Ali, Moonis; Kwon, Young Sig; Lee, Chang-Hwan; Kim, Juntae; Kim, Yongdai
Titel Konferenzpublikation:
Current Approaches in Applied Artificial Intelligence
Untertitel Konferenzpublikation:
28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings
Reihentitel:
Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence
Bandnummer Reihe:
9101
Konferenztitel:
International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (28., 2015, Seoul)
Konferenztitel:
IEA/AIE 2015
Tagungsort:
Seoul, South Korea
Jahr der Konferenz:
2015
Datum Beginn der Konferenz:
10.06.2015
Datum Ende der Konferenz:
12.06.2015
Verlagsort:
Cham
Verlag:
Springer
Jahr:
2015
Seiten von - bis:
305-315
Sprache:
Englisch
Abstract:
Opinion mining from physician rating websites depends on the quality of the extracted information. Sometimes reviews are user-error prone and the assigned stars or grades contradict the associated content. We therefore aim at detecting random individual error within reviews. Such errors comprise the disagreement in polarity of review texts and the respective ratings. The challenges that thereby arise are (1) the content and sentiment analysis of the review texts and (2) the removal of the random individual errors contained therein. To solve these tasks, we assign polarities to automatically recognized opinion phrases in reviews and then check for divergence in rating and text polarity. The novelty of our approach is that we improve user-generated data quality by excluding error-prone reviews on German physician websites from average ratings. «
Opinion mining from physician rating websites depends on the quality of the extracted information. Sometimes reviews are user-error prone and the assigned stars or grades contradict the associated content. We therefore aim at detecting random individual error within reviews. Such errors comprise the disagreement in polarity of review texts and the respective ratings. The challenges that thereby arise are (1) the content and sentiment analysis of the review texts and (2) the removal of the random... »