Sentiment classification on polarity reviews: an empirical study using rating-based features

Dai Quoc Nguyen, Dat Quoc Nguyen, Thanh Vu, Son Bao Pham

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

17 Citations (Scopus)


We present a new feature type named rating-based feature and evaluate the contribution of this feature to the task of document-level sentiment analysis. We achieve state-of-the-art results on two publicly available standard polarity movie datasets: on the dataset consisting of 2000 reviews produced by Pang and Lee (2004) we obtain an accuracy of 91.6% while it is 89.87% evaluated on the dataset of 50000 reviews created by Maas et al. (2011). We also get a performance at 93.24% on our own dataset consisting of 233600 movie reviews, and we aim to share this dataset for further research in sentiment polarity analysis task.
Original languageEnglish
Title of host publicationACL 2014
Subtitle of host publication5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis: proceedings of the workshop
Place of PublicationPennsylvania
PublisherAssociation for Computational Linguistics (ACL)
Number of pages8
ISBN (Print)9781941643112
Publication statusPublished - 1 Jun 2014
Externally publishedYes


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