Challenges for Information Retrieval in Big data: Product Review Context

The ever increasing scale of e-commerce has today presented a big range of choice for the customer. Customer uses online product reviews as a primary criterion to make a decision for his purchase. These product reviews are scattered all around the internet, and this data has a great potential value. However, it is also unstructured and written in a natural language, which poses great problems for data mining and data analytics. The scale, non-uniformity and complexity of product reviews make them classic big data elements. This paper discusses the big data challenges and opportunities involved in mining and analytics of product review data. It formally studies the problem under a big data framework and formulates a plan for the extraction, mining and analysis. This paper also reviews some of the mining approaches for product reviews and implemented feature/attributes based method for finding the review of products.

[1]  Khaled M. Fouad,et al.  Semantic Retrieval Approach for Web Documents , 2011 .

[2]  Satoshi Morinaga,et al.  Mining product reputations on the Web , 2002, KDD.

[3]  James Parker,et al.  on Knowledge and Data Engineering, , 1990 .

[4]  David L. Olson,et al.  Advanced Data Mining Techniques , 2008 .

[5]  Ray Bert.  Ideas: A History of Thought and Invention, from Fire to Freud , 2005 .

[6]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[7]  Pan Ying,et al.  Building Intelligent Information Retrieval System Based on Ontology , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[8]  Xuanjing Huang,et al.  Mining product reviews based on shallow dependency parsing , 2009, SIGIR.

[9]  Wenjie Li,et al.  Semantic Web-Oriented Intelligent Information Retrieval System , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[10]  Tianfang Yao,et al.  Combining dependency parsing with shallow semantic analysis for Chinese opinion-element relation identification , 2010, 2010 4th International Universal Communication Symposium.

[11]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[12]  Xuanjing Huang,et al.  Structural Opinion Mining for Graph-based Sentiment Representation , 2011, EMNLP.

[13]  Timothy W. Finin,et al.  Swoogle: a search and metadata engine for the semantic web , 2004, CIKM '04.

[14]  M. A. Ansari,et al.  A Survey: Soft Computing in Intelligent Information Retrieval Systems , 2012, 2012 12th International Conference on Computational Science and Its Applications.

[15]  Xuanjing Huang,et al.  Phrase Dependency Parsing for Opinion Mining , 2009, EMNLP.

[16]  Jure Leskovec,et al.  Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.

[17]  Fan Zhang,et al.  Intelligent Information Retrieval Model Based on Multi-Agents , 2007, 2007 International Conference on Wireless Communications, Networking and Mobile Computing.

[18]  Seref Sagiroglu,et al.  Big data: A review , 2013, 2013 International Conference on Collaboration Technologies and Systems (CTS).

[19]  Li Ding,et al.  Trust based knowledge outsourcing for semantic Web agents , 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

[20]  Harry Chen,et al.  ITTALKS: A Case Study in the Semantic Web and DAML , 2001, SWWS.

[21]  S. R,et al.  Data Mining with Big Data , 2017, 2017 11th International Conference on Intelligent Systems and Control (ISCO).

[22]  Yong-Min Liu,et al.  Artificial Intelligent Information Retrieval Using Assigning Context of Documents , 2009, 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing.

[23]  Jianguo Jiang,et al.  The technology of intelligent information retrieval based on the semantic web , 2010, 2010 2nd International Conference on Signal Processing Systems.

[24]  Alin Deutsch,et al.  XML-QL: A Query Language for XML , 1998 .

[25]  John Davies,et al.  QuizRDF: search technology for the semantic Web , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[26]  Nicholas J. Belkin,et al.  Intelligent Information Retrieval: Whose Intelligence? , 1996, ISI.

[27]  S. Kalaivani,et al.  Comparison of Question Answering Systems Based on Ontology and Semantic Web in Different Environment , 2012 .

[28]  Claire Cardie,et al.  Combining Low-Level and Summary Representations of Opinions for Multi-Perspective Question Answering , 2003, New Directions in Question Answering.

[29]  Xiaoqing Huang,et al.  E-Business in Developing Countries: A Comparison of China and India , 2007, Int. J. E Bus. Res..