Sentiment Analysis in Scholarly Book Reviews
This addresses sentiment analysis for scholarly book reviews, an incremental domain-specific application.
The authors tackled sentiment analysis in scholarly book reviews by combining features for supervised classifiers to extract opinion targets and polarities, constructing a labeled French corpus and evaluating on English restaurant reviews, showing robustness across domains and languages.
So far different studies have tackled the sentiment analysis in several domains such as restaurant and movie reviews. But, this problem has not been studied in scholarly book reviews which is different in terms of review style and size. In this paper, we propose to combine different features in order to be presented to a supervised classifiers which extract the opinion target expressions and detect their polarities in scholarly book reviews. We construct a labeled corpus for training and evaluating our methods in French book reviews. We also evaluate them on English restaurant reviews in order to measure their robustness across the domains and languages. The evaluation shows that our methods are enough robust for English restaurant reviews and French book reviews.