Product Features Extraction from Opinions According to Time
Nowadays, e-commerce shopping websites have experienced noticeable growth. These websites have gained consumers’ trust. After purchasing a product, many consumers share comments where opinions are usually embedded about the given product. Research on the automatic management of opinions that gives suggestions to potential consumers and portrays an image of the product to manufactures has been growing recently. After launching the product in the market, the reviews generated around it do not usually contain helpful information or generic opinions about this product (e.g. telephone: great phone...); in the sense that the product is still in the launching phase in the market. Within time, the product becomes old. Therefore, consumers perceive the advantages/ disadvantages about each specific product feature. Therefore, they will generate comments that contain their sentiments about these features. In this paper, we present an unsupervised method to extract different product features hidden in the opinions which influence its purchase, and that combines Time Weighting (TW) which depends on the time opinions were expressed with Term Frequency-Inverse Document Frequency (TF-IDF). We conduct several experiments using two different datasets about cell phones and hotels. The results show the effectiveness of our automatic feature extraction, as well as its domain independent characteristic.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125349Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 993
 Rodrigo Moraes, João Francisco Valiati, Wilson P. Gavião Neto, “Document-level sentiment classification: An empirical comparison between SVM and ANN,“ Expert Systems with Applications 40, 2013, pp 621–633.
 Sylvester Olubolu Orimaye et al., “Buy It – Don’t Buy It: Sentiment Classification on Amazon Reviews Using Sentence Polarity Shift,” In: Proceedings of International Conference on Artificial Intelligence, Kuching, Malaysia,2012, pp 386-399.
 Kushal Bafna and Durga Toshniwal, “Feature based Summarization of Customers’ Reviews of Online Products,” Procedia Computer Science 22,2013, pp 142-151.
 Hasnae Rahimi and Hanan EL Bakkali, “CIOSOS: Combined Idiomatic-Ontology Based Sentiment Orientation System for Trust Reputation in E-commerce,” International Joint Conference,2015, pp 189-200.
 Ruihai Dong, Michael P. O’Mahony, Markus Schaal, Kevin McCarthy, Barry Smyth, “Combining similarity and sentiment in opinion mining for product recommendation,” Journal of Intelligent Information Systems,2014, pp 1-28.
 Minqing Hu and Bing Liu, “Mining and summarizing customer reviews,” In Proceedings of 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’04) USA, 2004.
 Saruladha. K, Banupriya. D, Nargis Banu. J, “Opinion Summary Generation for Product Reviews,” International Journal of Engineering Research & Technology (IJERT). Vol. 3 - Issue 3, 2014.
 Y. Wu, Q. Zhang, X. Huang, L. Wu, “Phrase dependency parsing for opinion mining,” Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, (Singapore),2009, pp. 1533–1541.
 Z. Yan, ET AL., “EXPRS: An extended pagerank method for product feature extraction from online consumer reviews,” Inf. Manage. Available from: http://dx.doi.org/10.1016/j.im.2015.02.002.
 Ravi Kumar V. and K. Raghuveer, “Dependency Driven Semantic Approach to Product Features Extraction and Summarization Using Customer Reviews,” In Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY), Chennai, India - Volume 3, 2012, pp 225-238.
 Qadir, A.,”Detecting Opinion Sentences Specific to Product Features in Customer Reviews using Typed Dependency Relations,” In: Events in Emerging Text Types (eETTs), Borovets, Bulgaria, 2009, pp. 38–43.
 Silviu Homoceanu, Michael Loster, Christoph Lofi and Wolf-Tilo Balke, “Will I like it? – Providing Product Overviews based on Opinion Excerpts,” IEEE Conference on Commerce and Enterprise Computing (CEC), Luxembourg, 2011.
 Changqin Quan and Fuji Ren, “Unsupervised product feature extraction for feature-oriented opinion determination,” Information Sciences 272, 2014, pp 16-28.
 Wei Wei and Jon Atle Gulla, “Sentiment learning on product reviews via sentiment ontology tree,” Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, Sweden, 2010, pp. 404–413. The Association for Computer Linguistics.
 Moreno, V., Fraga, A., Sanchez-Cervantes, J.L., “Feature-Based Opinion Mining through ontologies,” Expert Systems with Applications, 2014.
 Lili Zhao and Chunping Li, “Ontology Based Opinion Mining for Movie Reviews,” Volume 5914 of the series Lecture Notes in Computer Science, 2009, pp 204-214.
 Liu Li-zhen et al., “Generating Domain-Specific Affective Ontology from Chinese Reviews for Sentiment Analysis,” Journal of Shanghai Jiaotong University (Science), 2015, pp 32-37.
 Zhichao Li, Min Zhang, Shaoping Ma, Bo Zhou, Yu Sun, “Automatic Extraction for Product Feature Words from Comments on the Web,” 5th Asia Information Retrieval Symposium, Sapporo, Japan, 2009, pp 112-123.
 Hu, M., Liu, B., “Mining opinion features in customer reviews,” In: Proceedings of AAAI, 2004, pp. 755–760.
 H. Nakagawa and T. Mori, “A simple but powerful automatic term extraction method,” Int. Workshop on Computational Terminology, Morristown, NJ, USA, 2002.
 Ana-Maria Popescu, Oren Etzioni, “Extracting product features and opinions from reviews,” Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, 2005, pp.339-346, Canada.
 D. Wang, Shenghuo Zhu and Tao Li, “SumView: A Web-based engine for summarizing product reviews and customer opinions,” Journal: Expert System with Application 40, 2013, pp. 23-37.
 Language Detection with Infinity-gram. https://github.com/shuyo/language-detection.
 Spelling suggestions for text. http://wsf.cdyne.com/SpellChecker/check.asmx.
 Stanford Natural Language Processing Group. http://nlp.stanford.edu/software/tagger.shtml.
 S. Baccianella, A. Esuli, and F. Sebastiani, “SENTIWORD NET 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining,” 7th Conf on International Language Resources and Evaluation (LREC), Marrakech, Morocco: European Language Resources Association (ELRA), 2008.
 Rakesh Agrawal Ramakrishnan Srikant, “Fast Algorithms for Mining Association Rules,” Proceedings of the 20th VLDB Conference Santiago, Chile, 1994.
 Weisstein, E.W. Logistic Equation. (MathWorld A Wolfram Web Resource, 2003). http://mathworld.wolfram.com/LogisticEquation.html