An unsupervised sentiment classifier on summarized or full reviews

Abstract

These days web users searching for opinions expressed by others on a particular product or service PS can turn to review repositories, such as Epinions.com or Imdb.com. While these repositories often provide a high quantity of reviews on PS, browsing through archived reviews to locate different opinions expressed on PS is a time-consuming and tedious task, and in most cases, a very labor-intensive process. To simplify the task of identifying reviews expressing positive, negative, and neutral opinions on PS, we introduce a simple, yet effective sentiment classifier, denoted SentiClass, which categorizes reviews on PS using the semantic, syntactic, and sentiment content of the reviews. To speed up the classification process, SentiClass summarizes each review to be classified using eSummar, a single-document, extractive, sentiment summarizer proposed in this paper, based on various sentence scores and anaphora resolution. SentiClass (eSummar, respectively) is domain and structure independent and does not require any training for performing the classification (summarization, respectively) task. Empirical studies conducted on two widely-used datasets, Movie Reviews and Game Reviews, in addition to a collection of Epinions.com reviews, show that SentiClass (i) is highly accurate in classifying summarized or full reviews and (ii) outperforms well-known classifiers in categorizing reviews. © 2010 Springer-Verlag Berlin Heidelberg.