FEROM: A Pragmatic Approach for Sentiment Analysis and Opinion Mining

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Dhanashree Rajiv Raut
Sachin Chavan


In recent years, online shopping has become popular as it is convenient, reliable and cost effective. The online customer finds it difficult to make purchasing decisions based on the pictures or descriptions provided. The online review often makes it easy for the customer to make decisions for purchasing products as they are a great source to compare products and features. Unfortunately, going through all customer reviews is difficult, especially for popular items as they are in a number of hundreds or thousands.Now-a-days, a large number of availability of rich opinion resources like online review sites and blogs helps customers to understand the opinions of others about the product.We have proposed a system Feature Extraction and Refinement for Opinion Mining (FEROM) which aims to mine customer reviews of a product and extract high detailed product entities on which reviewers express their opinions. The opinions expressed by the customer are reviewed and then they are divided into multiple sentences. The Parts of Speech (POS) tagging is applied on these sentences where each sentence is tagged according to its respective parts of speech. After tagging, their expressions are identified with the help of SentiwordNet dictionary. The words in each sentence are assigned a score and an objective score is calculated with the help of SentiwordNet Dictionary.The sentiment of the customer is identified and opinion orientations for each recognized product entity are classified. These words are then compared with the dictionary of positive and negative which finally segregates the reviews into positive and negative.


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Author Biographies

Dhanashree Rajiv Raut, MGM's College of Engineering and Technology, Mumbai University

Student,Computer Department

Sachin Chavan, MGM's College of Engineering and Technology, Mumbai University

Professor, Computer Department


Erik Cambria, Bjorn Schuller, Yunqing Xia, Catherine Havasi, “New Avenues in Opinion Mining and Sentiment Analysis,†IEEE Intelligent system, Vol. 28, No. 2, March-April 2013, pp. 15-21.

Kunpeng Zhang, Yu Cheng,Wei-keng Liao, Alok Choudhary, “Mining Millions of Reviews: A Technique to Rank Products Based on Importance of Reviews,†in Proceedings of the 13th International Conference on Electronic Commerce, August 2011.

Ramanathan Narayanan ,Kunpeng Zhang, Alok Choudhary, “Voice of the Customers: Mining Online Customer Reviews for Product Feature-based Ranking†in Proceedings of the 3rd conference on Online Social Networks, June 2010.

G. Di Fabbrizio, A. Aker, and R. Gaizauskas, “STARLET: Multi-Document Summarization of Service and Product Reviews with Balanced Rating Distributions,†11th IEEE International Conference on Data Mining Workshops, 11-11 Dec. 2011, pp. 67–74.

Hideki Asoh,Chihiro Ono,Yoichi Motomura, “A Movie Recommendation Method Considering Both User’s Personality and Situation,†Workshop on Recommender Systems, Riva del Garda, Italy, August 28-29, 2006.

Yin-Fu Huang, Heng Lin, “Web Product Ranking Using Opinion Mining,†IEEE Symposium on Computational Intelligence and Data Mining, 16-19 April. 2013, pp. 184-190.

Weishu Hu, Zhiguo Gong, Jingzhi Guo, “Mining Product Features from Online Reviews,†7th IEEE International Conference on E-Business Engineering, 10-12 Nov. 2010, pp. 24-29.

Lizhen Liu, Zhixin Lv, Hanshi Wang, “Opinion Mining Based on Feature-Level,†in 5th IEEE International Congress on Image and Signal Processing, 16-18 Oct. 2012, pp. 1596-1600.

Liu Gongshen, Lai Huoyao,Luo Jun, Lin Jiuchuan, “Predicting the Semantic Orientation of Movie Reviews,†Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 10-12 Aug. 2010, pp.2483-2488.

Heng-Liyang , Qing-Fenglin , “Sentiment Analysis In Multi-scenarios: Using Evolution Strategies For Optimization,†Proceedings of the 2013 International Conference on Machine Learning and Cybernetics, Tianjin, 14-17 July, 2013,pp. 1230-1233.

Balla-Müller Nóra, Camelia Lemnaru, Rodica Potolea“Semi-Supervised Learning with Lexical Knowledge for Opinion Mining,†IEEE 6th International Conference on Intelligent Computer Communication and Processing, 26-28 August 2010.

D. Olsher, “Full Spectrum Opinion Mining: Integrating Domain, Syntactic and Lexical Knowledge,†12th IEEE International Conference on Data Mining Workshops, 10-10 Dec. 2012, pp. 693–700.

Gamgarn Somprasertsri, Pattarachai Lalitrojwong, “Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization,†Journal of Universal Computer Science, Vol. 16, No. 6, 2010, pp. 938-955.

Haseena Rahmath P, “Opinion Mining and Sentiment Analysis - Challenges and Applications,†International Journal of Application or Innovation in Engineering & Management, May 2014, Volume 3, Issue 5,pp. 401-403.

G.Vinodhini, RM.Chandrasekaran, “Sentiment Analysis and Opinion Mining: A Survey,†International Journal of Advanced Research in Computer Science and Software Engineering, June 2012, Vol.2, Issue 6, pp. 282-292.