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Conference Paper: Sentiment-based topic suggestion for micro-reviews

TitleSentiment-based topic suggestion for micro-reviews
Authors
Issue Date2016
PublisherAssociation for the Advancement of Artificial Intelligence .
Citation
The 10th International Conference on Web and Social Media (ICWSM 2016), Cologne, Germany, 17-20 May 2016. In Conference Proceedings, 2016, p. 231-240 How to Cite?
AbstractLocation-based social sites, such as Foursquare or Yelp, are gaining increasing popularity. These sites allow users to check in at venues and leave a short commentary in the form of a micro-review. Micro-reviews are rich in content as they offer a distilled and concise account of user experience. In this paper we consider the problem of predicting the topic of a micro-review by a user who visits a new venue. Such a prediction can help users make informed decisions, and also help venue owners personalize users' experiences. However, topic modeling for micro-reviews is particularly difficult, due to their short and fragmented nature. We address this issue using pooling strategies, which aggregate micro-reviews at the venue or user level, and we propose novel probabilistic models based on Latent Dirichlet Allocation (LDA) for extracting the topics related to a user-venue pair. Our best topic model integrates influences from both venue inherent properties and user preferences, considering at the same the sentiment orientation of the users. Experimental results on real datasets demonstrate the superiority of this model compared to simpler models and previous work; they also show that venue-inherent properties have higher influence on the topics of micro-reviews. © Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/229724
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLu, Z-
dc.contributor.authorMamoulis, N-
dc.contributor.authorPitoura, E-
dc.contributor.authorTsaparas, P-
dc.date.accessioned2016-08-23T14:12:53Z-
dc.date.available2016-08-23T14:12:53Z-
dc.date.issued2016-
dc.identifier.citationThe 10th International Conference on Web and Social Media (ICWSM 2016), Cologne, Germany, 17-20 May 2016. In Conference Proceedings, 2016, p. 231-240-
dc.identifier.isbn978-157735758-2-
dc.identifier.urihttp://hdl.handle.net/10722/229724-
dc.description.abstractLocation-based social sites, such as Foursquare or Yelp, are gaining increasing popularity. These sites allow users to check in at venues and leave a short commentary in the form of a micro-review. Micro-reviews are rich in content as they offer a distilled and concise account of user experience. In this paper we consider the problem of predicting the topic of a micro-review by a user who visits a new venue. Such a prediction can help users make informed decisions, and also help venue owners personalize users' experiences. However, topic modeling for micro-reviews is particularly difficult, due to their short and fragmented nature. We address this issue using pooling strategies, which aggregate micro-reviews at the venue or user level, and we propose novel probabilistic models based on Latent Dirichlet Allocation (LDA) for extracting the topics related to a user-venue pair. Our best topic model integrates influences from both venue inherent properties and user preferences, considering at the same the sentiment orientation of the users. Experimental results on real datasets demonstrate the superiority of this model compared to simpler models and previous work; they also show that venue-inherent properties have higher influence on the topics of micro-reviews. © Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.-
dc.languageeng-
dc.publisherAssociation for the Advancement of Artificial Intelligence .-
dc.relation.ispartofProceedings of the 10th International AAAI Conference on Web and Social Media, ICWSM 2016-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleSentiment-based topic suggestion for micro-reviews-
dc.typeConference_Paper-
dc.identifier.emailMamoulis, N: nikos@cs.hku.hk-
dc.identifier.authorityMamoulis, N=rp00155-
dc.description.naturepostprint-
dc.identifier.scopuseid_2-s2.0-84979608972-
dc.identifier.hkuros262989-
dc.identifier.spage231-
dc.identifier.epage240-
dc.publisher.placeUnited states-
dc.customcontrol.immutablesml 160919-

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