Opinion Mining of Product Features with Customer
Jawahar Gawade1, Latha Parthiban2

1Jawahar Gawade, Research Scholar, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2Latha Parthiban, Department of Computer Science, Pondicherry University, CC, India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 02 March 2019 | Manuscript Published on 08 June 2019 | PP: 59-64 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10120275S419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: With the fast growth in e-commerce, surveys for famous products on the web have grown rapidly. Although these reviews are significant in making decisions, it is difficult to read all reviews. Automation of emotion mining method was the well-known answer to the dilemma. Although there are algorithms for emotion mining, an algorithm with evolving accuracy is needed. An algorithm which extracts product traits from surveys based on traits frequency and generates a view on item traits is developed and tested on downloaded buyer review. The sentences were tagged, sentiment words were extracted and view orientations were identified using the semantic orientation of notion terms. The precision values for traits extraction and both precision and recall values for view orientation recognition were significantly improved by the proposed algorithm.
Keywords: Opinion Mining; Customer Reviews; Sentiment Analysis; Sentiment Classification.
Scope of the Article: Data Mining