Accurate Information Extraction from Customer Comments Posted Online
Mishu Jain1, Rajni Jindal2
1Mishu Jain, Chief Engineer, Senior Professional Working in Samsung Electronics, Department of Computer Engineering, Delhi Technological University Delhi, India.
2Dr. Rajni Jindal, Head, Department of Computer Engineering, Delhi Technological University Delhi, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 2151-2153 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7731118419/2019©BEIESP | DOI: 10.35940/ijrte.D7731.118419
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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: Customer comments form an integral part for identification of failures and success of a product. Buying patterns of a customer greatly depends on the pattern of comments posted online. Online review/comments can be broadly classified into positive, negative and neutral. Many tools available in market can be used for their classification. However, there are various flaws in classifying methods that can tweak the result of these comments such as “Unidentified/Hidden information in neutral comments”, “Wrong keyword extraction while splitting words”, “fake comments based on frequency of duplicate comment or reviewer”. This paper addresses this problem based on online product comments posted on Amazon website and proposes an ideal flow chart and algorithm to address these problems.
Keywords: Polarity, Customer Comments, Drill Down, Opinion Mining.
Scope of the Article: Online Learning Systems.