Abstract
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 language | English |
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Title of host publication | ACL 2014 |
Subtitle of host publication | 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis: proceedings of the workshop |
Place of Publication | Pennsylvania |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 128-135 |
Number of pages | 8 |
ISBN (Print) | 9781941643112 |
Publication status | Published - 1 Jun 2014 |
Externally published | Yes |