Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Rated aspect summarization of short comments
Proceedings of the 18th international conference on World wide web
Latent aspect rating analysis on review text data: a rating regression approach
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Hi-index | 0.00 |
With the opinion explosion on Web, there are growing research interests in opinion mining. In this study we focus on an important problem in opinion mining -- Aspect Identification (AI), which aims to extract aspect terms in entity reviews. Previous PLSA based AI methods exploit the 2-tuples (e.g. the co-occurrence of head and modifier), where each latent topic corresponds to an aspect. Here, we notice that each review is also accompanied by an entity and its overall rating, resulting in quad-tuples joined with the previously mentioned 2-tuples. Believing that the quad-tuples contain more co-occurrence information and thus provide more ability in differentiating topics, we propose a model of Quad-tuple PLSA, which incorporates two more items -- entity and its rating, into topic modeling for more accurate aspect identification. The experiments on different numbers of hotel and restaurant reviews show the consistent and significant improvements of the proposed model compared to the 2-tuple PLSA based methods.