A Hierarchical Classifier Applied to Multi-way Sentiment Detection

This paper considers the problem of document-level multi-way sentiment detection, proposing a hierarchical classifier algorithm that accounts for the inter-class similarity of tagged sentiment-bearing texts. This type of classifier also provides a natural mechanism for reducing the feature space of the problem. Our results show that this approach improves on state-of-the-art predictive performance for movie reviews with three-star and fourstar ratings, while simultaneously reducing training times and memory requirements.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Adrian Bickerstaffe, Ingrid Zukerman
• Keywords: COLING 2010, Computational Linguistics, Document-level Multi-way Sentiment, Hierarchical Classifier Algorithm, State-of-the-art Predictive Performance