Feature Selection Methods in Sentiment Analysis and Sentiment Classification of Amazon Product Reviews

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-36 Number-4
Year of Publication : 2016
Authors : Tahura Shaikh, Dr. Deepa Deshpande
  10.14445/22312803/IJCTT-V36P139

MLA

Tahura Shaikh, Dr. Deepa Deshpande "Feature Selection Methods in Sentiment Analysis and Sentiment Classification of Amazon Product Reviews". International Journal of Computer Trends and Technology (IJCTT) V36(4):225-230 June 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Sentiment Analysis or Opinion Mining is a nascent field of data mining, which is expanding and much research work is being done in this field. Opinion Mining mines people’s opinion towards a topic. Opinion mining’s main objective is to extract opinion or views of a person for a particular topic or subject. Mainly Opinion Mining classifies the given review as positive, neutral or negative. Opinion Mining has accomplished much focus nowadays due to availability of vast amount of opinion rich web resources such as online product reviews, blogs, social networking sites etc. As the use of ecommerce websites are increasing profusely and people are opting for online shopping there is vast amount of data generated which can be useful for Opinion Mining. In this paper, different feature extraction or selection techniques for opinion mining are performed. Work is carried out in different steps. First step is the data collection step in which amazon dataset is used. Second is the pre-processing step which is used for the removal of stop words and special characters. In the third step, feature selection or extraction techniques like phrase level, single word and multiword are applied over the amazon dataset. The fourth step is used to generate the vector of the extracted features. In the final step, Naïve Bayes classifier is applied to classify the reviews. Step one to four is used for training the system and last step is used for testing. In the paper Supervised learning method is used for classification of reviews.

References
[1] J. Ashok Kumar and S. Abirami, "An Experimental Study Of Feature Extraction Techniques In Opinion Mining," International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.4, No.1, February 2015.
[2] GurneetKaur and AbhinashSingla, “Sentimental Analysis of Flipkart reviews using Naïve Bayes and Decision Tree algorithm,” International Journal Of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 5, ISSN: 2278- 1323, January 2016.
[3] Mohamed Ibrahim Abouelhoda , Stefan Kurta and Enno Ohlebusch,”Replacing suffix trees with enhanced suffix arrays”, 1570-8667, 2004.
[4] Aashutosh Bhatt, Ankit Patel, Harsh Chheda and Kiran Gawande, "Amazon Review Classification and Sentiment Analysis", International Journal of Computer Science and Information Technologies, Vol. 6, ISSN:0975-9646, 2015.
[5] Jeevanandam Jotheeswaran and S. Koteeswaran,“Feature Selection using Random Forest method for Sentiment Analysis,” Indian Journal of Science and Technology, vol.9. ISSN : 0974-5645, Jan. 2016.
[6] SuSuHtay and KhinThidarLynn , “Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Review,” Hindawi Publishing Corporation TheScientific World Journal, Vol.2013.
[7] Lisette García-Moya, Henry Anaya-Sánchez, and Rafael Berlanga-Llavori, “Retrieving Product Features and Opinions from Customer Reviews”, IEEE Intelligent Systems, vol. 28, pp. 1541-1672, May/Jun 2013.
[8] Tina R. Patil and Mrs. S. S. Sherekar, “Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification,” International Journal Of Computer Science And Applications, vol. 6, ISSN: 0974-1011, Apr 2013.
[9] Dim En Nyaung, Thin Lai Lai Thein, "Feature Based Summarizing From Customer Reviews", International Journal Of Scientific Engineering and Technology Research, vol.03, ISSN 2319-8885, Dec-2014.
[10] Bing Liu, Sentiment analysis and opinion mining, Morgan & Claypool Publishers, 2012
[11] R Krishna, Kavitha S, Yamini S,Rekha A,“Analysis of Various Opinion Mining Algorithms Gayathri,” International Journal of Computer Trends and Technology, vol. 22 , Number 2, ISSN: 2231-2803 ,April 2015.

Keywords
Opinion Mining, Sentiment Analysis, User Reviews, Feature Extraction, Classification, Naïve Bayes.