International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 8, No. 1, 8 Jan. 2018
Cover page and Table of Contents: PDF (size: 303KB)
A Combined Approach for Effective Features Extraction from Online Product Reviews
Full Text (PDF, 303KB), PP.11-21
Views: 0 Downloads: 0
Author(s)
Index Terms
E-commerce, online reviews, opinion mining, NLP, Topic Modeling
Abstract
Today E-commerce websites provide customers with the needed product information by giving various kinds of services to choose from. One such service is to allow the customer to read the end user online reviews. Online reviews contain features which are useful for the analysis in opinion mining. Converting these unstructured reviews into useful information require extracting the product features from them. Natural Language Processing (NLP) based technique extracts various kinds of product features including the low frequency features. Topic Modeling based approach also identifies specific product features from the online reviews. The effective number of product features is made available to the customer when these two approaches are combined. This results in the expanded product feature set so that the customer makes wise decisions without having to compromise on the feature set.
Cite This Paper
D. Teja Santosh,"A Combined Approach for Effective Features Extraction from Online Product Reviews", International Journal of Education and Management Engineering(IJEME), Vol.8, No.1, pp.11-21, 2018. DOI: 10.5815/ijeme.2018.01.02
Reference
[1] Hu, Minqing, and Bing Liu. "Mining and summarizing customer reviews."Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2004.
[2] Hu, Minqing, and Bing Liu. "Mining opinion features in customer reviews."AAAI. Vol. 4. No. 4. 2004.
[3] Popescu, Ana-Maria, and Orena Etzioni. "Extracting product features and opinions from reviews." Natural language processing and text mining. Springer London, 2007. 9-28.
[4] Raju, Santosh, Prasad Pingali, and Vasudeva Varma. "An unsupervised approach to product attribute extraction." Advances in Information Retrieval. Springer Berlin Heidelberg, 2009. 796-800.
[5] Liu, Bing, Minqing Hu, and Junsheng Cheng. "Opinion observer: analyzing and comparing opinions on the web." Proceedings of the 14th international conference on World Wide Web. ACM, 2005.
[6] Zhuang, L., F. Jing, and X. Zhu. Movie review mining and summarization. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2006), 2006.
[7] Wu, Yuanbin, et al. "Phrase dependency parsing for opinion mining."Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3. Association for Computational Linguistics, 2009.
[8] Santosh, D. Teja, and B. Vishnu Vardhan. "OBTAINING FEATURE-AND SENTIMENT-BASED LINKED INSTANCE RDF DATA FROM UNSTRUCTURED REVIEWS USING ONTOLOGY-BASED MACHINE LEARNING." International Journal of Technology (2015) 2: 198 206.
[9] Mei, Qiaozhu, et al. "Topic sentiment mixture: modeling facets and opinions in weblogs." Proceedings of the 16th international conference on World Wide Web. ACM, 2007.
[10] Titov, Ivan, and Ryan McDonald. "Modeling online reviews with multi-grain topic models." Proceedings of the 17th international conference on World Wide Web. ACM, 2008.
[11] Lin, Chenghua, and Yulan He. "Joint sentiment/topic model for sentiment analysis." Proceedings of the 18th ACM conference on Information and knowledge management. ACM, 2009.
[12] Zhan, Tian-Jie, and Chun-Hung Li. "Semantic dependent word pairs generative model for fine-grained product feature mining." Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2011. 460-475.
[13] Ma, Baizhang, et al. "An LDA and Synonym Lexicon Based Approach to Product Feature Extraction from Online Consumer Product Reviews."Journal of Electronic Commerce Research 14.4 (2013): 304.
[14] Guzman, Emitza, and Wiem Maalej. "How do users like this feature? a fine grained sentiment analysis of app reviews." Requirements Engineering Conference (RE), 2014 IEEE 22nd International. IEEE, 2014.
[15] D. Teja, B. Vishnu Vardhan, and D. Ramesh. "Extracting Product Features from Reviews Using Feature Ontology Tree Applied on LDA Topic Clusters." Advanced Computing (IACC), 2016 IEEE 6th International Conference on. IEEE, 2016.
[16] Liu, Bing. "Opinion mining." Encyclopedia of Database Systems. Springer US, 2009. 1986-1990.
[17] Christiane Fellbaum (1998), WordNet: An Electronic Lexical Database. Bradford Books.
[18] Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani, SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining, In LREC, vol. 10, pp. 2200-2204. 2010.
[19] Zhang, Lei, and Bing Liu. "Aspect and entity extraction for opinion mining."Data mining and knowledge discovery for big data. Springer Berlin Heidelberg, 2014. 1-40.
[20] Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent dirichlet allocation." the Journal of machine Learning research 3 (2003): 993-1022.