Abstract

Opinion mining plays a most important role in text mining applications in brand and product positioning, customer relationship management, consumer attitude detection and market research. The applications lead to new generation of companies/products meant for online market perception, online content monitoring and reputation management. Expansion of the web inspires users to contribute/express opinions via blogs, videos and social networking sites. Such platforms provide valuable information for analysis of sentiment pertaining a product or service. This study investigates the performance of various feature extraction methods and classification algorithm for opinion mining. Opinions expressed in Amazon website for cameras are collected and used for evaluation. Features are extracted from the opinions using Term Document Frequency and Inverse Document Frequency (TDF×IDF). Feature transformation is achieved through Principal Component Analysis (PCA) and kernel PCA. Naïve Bayes, K Nearest Neighbor and Classification and Regression Trees (CART) classification algorithms classify the features extracted.

Keywords:

K nearest neighbor and Classification and Regression Trees (CART), na, opinion mining, Principal Component Analysis (PCA) and kernel PCA, TDF,

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Competing interests

The authors have no competing interests.

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