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Abstract
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Article Information:
Framework for Evaluating Camera Opinions
K.M. Subramanian and K. Venkatachalam
Corresponding Author: K.M. Subramanian
Submitted: September 29, 2014
Accepted: November 3, 2014
Published: March 05, 2015 |
Abstract:
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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 (TDFIDF). 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.
Key words: K nearest neighbor and Classification and Regression Trees (CART), naïve bayes, opinion mining, Principal Component Analysis (PCA) and kernel PCA, TDF&multiplyIDF , ,
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Cite this Reference:
K.M. Subramanian and K. Venkatachalam, . Framework for Evaluating Camera Opinions. Research Journal of Applied Sciences, Engineering and Technology, (7): 519-525.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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