A Novel Approach Based On Pattern Discovery and Supervised Learning to Identify Comparative Sentences

Abstract

This paper studies the problem of identifying comparative sentences in text documents. The problem is related to but quite different from sentiment/opinion sentence identification or classification. Sentiment classification studies the problem of classifying a document or a sentence based on the subjective opinion of the author. An important application area of sentiment/opinion identification is business intelligence as a product manufacturer always wants to know consumers’ opinions on its products. Comparisons on the other hand can be subjective or objective. Furthermore, a comparison is not concerned with an object in isolation. Instead, it compares the object with others. An example opinion sentence is “the sound quality of CD player X is poor”. An example comparative sentence is “the sound quality of CD player X is not as good as that of CD player Y”. Clearly, these two sentences give different information. Their language constructs are quite different too. Identifying comparative sentences is also useful in practice because direct comparisons are perhaps one of the most convincing ways of evaluation, which may even be more important than opinions on each individual object. This paper proposes to study the comparative sentence identification Problem. It first categorizes comparative sentences into different types, and then presents a novel integrated pattern discovery and supervised learning approach to identifying comparative sentences from text documents, indicative that lexicons built using semi supervised methods such as SentiWordNet can be an important resource in sentiment classification tasks. Considerations on future improvements are also presented based on a detailed analysis of classification results.