Mining and Summarizing Opinion Features in Movie Reviews
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Abstract
Sentiment analysis, also called opinion mining, is the field of study that analyzes people’s opinions, sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as movies, products, services, organizations, individuals, issues, events, topics, and their attributes. [1] For a particular movie, the number of reviews can be in hundreds or even thousands. This makes it difficult for a user to read and decide whether to watch the movie or not. So, we aim to mine and summarize all the user reviews of a movie. This summarization task is different from traditional text summarization because we only mine the features of the movie review on which the users have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining features that have been commented on by users; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks.
Keywords: Text mining, sentiment classification, summarization, reviews.
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