Sentiment Classification on Polarity Reviews: An Empirical Study Using Rating-based Features

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 analy-sis. We achieve state-of-the-art re-sults on two publicly available stan-dard polarity movie datasets: on the dataset consisting of 2000 reviews pro-duced by Pang and Lee (2004) we ob-tain 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 consist-ing of 233600 movie reviews, and we aim to share this dataset for further re-search in sentiment polarity analysis task. 1