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Investigating the Use of Extractive Summarisation in Sentiment Classification

In online reviews, authors often use a short passage to describe the overall feeling about a product or a service. A review as a whole can mention many details not in line with the overall feeling, so capturing this key passage is important to understand the overall sentiment of the review. This paper investigates the use of extractive summarisation in the context of sentiment classification. The aim is to find the summary sentence, or the short passage, which gives the overall sentiment of the review, filtering out potential noisy information. Experiments are carried out on a movie review data-set. The main finding is that subjectivity detection plays a central role in building summaries for sentiment classification. Subjective extracts carry the same polarity of the full text reviews, while statistical and positional approaches are not able to capture this aspect.