Feature Reduction using Principal Component Analysis for Opinion Mining

Opinions express viewpoints of users, and reviews gives information about how a product is perceived. Online reviews are now popularly used for judging quality of product or service and influence decision making of the users while selecting a product or service. Opinions are increasingly available in form of reviews and feedback at websites, blogs, and microblogs which influences future customers. As it is not feasible to manually handle the huge amount of opinions generated online, Opinion mining uses automatic processes for extracting reviews and discriminate relevant information with sentiment orientation. In this paper, it is proposed to extract the feature set from movie reviews. Inverse document frequency is computed and the feature set is reduced using Principal Component Analysis. The effectiveness of the pre-processing is evaluated using Naive Bayes and Linear Vector Quantization.

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