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Product Feature-based Ratings forOpinionSummarization of E-Commerce Feedback Comments

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Vijayshri Ramkrishna Ingale, Rajesh Nandkumar Phursule

Vijayshri Ramkrishna Ingale and Rajesh Nandkumar Phursule. Article: Product Feature-based Ratings forOpinionSummarization of E-Commerce Feedback Comments. International Journal of Computer Applications 135(8):14-18, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Vijayshri Ramkrishna Ingale and Rajesh Nandkumar Phursule},
	title = {Article: Product Feature-based Ratings forOpinionSummarization of E-Commerce Feedback Comments},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {135},
	number = {8},
	pages = {14-18},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


With the growth of internet, online social networking sites, blogs, discussion forums, etc have gained a tremendous importance. Consumers comment on net to express their views, feedbacks and opinions. The opinion of users is of great importance for mining useful information from the text which can be done through opinion mining techniques. Opinion mining or sentiment analysis is the computational field of study of people’s opinions, emotions, and attitude towards particular Feature. When buying a new product buyer mostly refer the opinion of the other users who have bought the product. Hence, in this work a product Feature rating framework is being proposed. This dissertation comprises mainly of four modules preprocessing, Feature identification, review classification and Feature rating. Finally, the rating are been shown in the graph. For the analysis of the system, we have used Amazon review dataset which consists of customers reviews about product. In the system Apriori algorithm is used for Feature identification, Support Vector Machine algorithm for review classification and SentiWordNet lexicon for giving rating to each Feature of the product.


  1. Xiuzhen Zhang, Lishan Cui and Yan Wang, “CommTrust: Computing Multi- Dimensional Trust By Mining E-Commerce Feedback Comments,” IEEE Transactions On Knowledge And Data Engineering, vol. 26, no. 7, pp. 1631-1643, 2014.
  2. Zheng-Jun Zha, Jianxing Yu, Meng Wang, Tat-Seng Chua," Product Aspect Ranking and Its Applications", IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 5, MAY 2014
  3. Abdul Wahab," IMPORTANT FEATURES SELECTION DURING SENTIMENT ANALYSIS", Sci.Int(Lahore),26(2),961-966,2014.
  4. Bing Liu. Sentiment Analysis and Opinion Mining, Morgan &
  5. Claypool Publishers, May 2012
  6. M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004, pp. 168-177.
  7. Wouter Bancken, Daniele Alfarone and Jesse Davis, “Automatically Detecting And Rating Product Features From Textual Customer Reviews,” Proceedings of DMNLP Workshop At ECML/PKDD, pp. 1-16, 2014.
  8. Bo Pang, Lillian Lee and Shivakumar Vaithyanathan, “Thumbs Up? Sentiment Classification Using Machine Learning Techniques,” Proceedings Of The Conference On Empirical Methods In Natural Language Processing (EMNLP), pp. 79-86, 2002.
  9. Zheng-Jun Zha, Jianxing Yu, Jinhui Tang, Meng Wang and Tat-Seng Chua, “Product Feature Ranking And Its Applications,” IEEE Transactions On Knowledge And Data Engineering, vol. 26, no. 5, pp. 1211-1224, 2014.
  10. Minqing Hu and Bing Liu, “Mining and Summarizing Customer Reviews,” 10th Proceeding ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 168-177, 2004.
  11. Jianxing Yu, Zheng-Jun Zha, MengWang and Tat-Seng Chua, “Feature ranking: Identifying important product Features from online consumer reviews,” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp. 1496-1505, 2011.
  12. Kumar, Ravi V. and K. Raghuveer, “Web User Opinion Analysis for Product Features Extraction and Opinion Summarization,” International Journal of Web and Semantic Technology, vol. 3, no. 4, pp. 69-82, 2012.
  13. Chien-Liang Liu, Wen-Hoar Hsaio, Chia-Hoang Lee, Gen-Chi Lu and Emery Jou, “Movie Rating And Review Summarization In Mobile Environment,” IEEE Transactions On Systems, Man, And Cybernetics, vol. 42, no. 3, pp. 397-407, 2012.
  14. Yuanbinwu, Qi Zhang, Xuanjing Huang and Lidewu, “Phrase Dependency Parsing For Opinion Mining,” Proceedings of the 2009 Conference On Empirical Methods in Natural Language Processing, pp. 1533-1541, 2009.
  15. Shenghua Bao, Shengliang Xu, Li Zhang, Rong Yan, Zhong Su, Dingyi Han and Yong Yu, “Mining Social Emotions from Affective Text,” IEEE transactions on knowledge and data engineering, vol. 24, no. 9, pp. 1658-1670, 2012.
  16. Mily Lal and Kavita Asnani, “Feature Extraction and Segmentation In Opinion Mining,” International Journal Of Engineering And Computer Science, vol. 3, no. 5, pp. 5873-5878, 2014.
  17. Mikalai Tsytsarau and Themis Palpanas, “Survey On Mining Subjective Data On The Web,” Data Mining Knowledge Discovery, Springer, pp. 478-514, 2012.
  18. Esuli and Sebastiani, “SentiWordNet: A publicly available resource for opinion mining,” In Proceedings of the 6th international conference on Language Resources and Evaluation (LREC06), pp. 417-422, 2006.
  19. Amani K Samha,Yuefeng Li and Jinglan Zhang , “Feature-Based Opinion Extraction From Customer Reviews,” arXiv preprint arXiv:1404.1982, pp. 149-160, 2014.


Opinion Mining, Sentiment Analysis, Feature