Improving Comparative Opinion Mining Through Detection of Support Sentences

Abstract

Comparative opinion contains contrasting views of products (e.g., which aspect of a product is better or worse). Most existing works for comparative opinion mining focus on single comparative sentences but have yet explored the benefits of additional comparative details in neighbouring sentences of a comparative sentence. Motivated by the needs to exploit these supporting details, this chapter proposes an approach to identify the link between a comparative sentence and its neighbouring sentences. As contextual similarity between comparative sentence and neighbouring sentence is crucial to determine their relatedness, contextual features of these two sentences are exploited to measure the similarity between them. Then, linear-chain conditional random field (CRF) is used to identify neighbouring sentence that is related to comparative sentence. Detection of supporting neighbouring sentence using linear-chain CRF with optimized contextual features (cosine similarity, Wordnet similarity, and comparative keywords) outperforms sentence similarity baseline by 4% in accuracy and 0.06 in F-score.