Competitor Identification by Use The Sentiment Classification Based on the User Research
V. Ajay Varma1, T. Ramesh Krishna2, Mercy Paul Selvan3

1V. Ajay Varma, UG Student, Department of CSE, Sathyabama University, Chennai (TamilNadu), India.

2T. Ramesh Krishna, UG Student, Department of CSE, Sathyabama University, Chennai (TamilNadu), India.

3Dr. Mercy Paul Selvan, Assistant professor, Department of CSE, Sathyabama University, Chennai (TamilNadu), India.

Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 31 August 2019 | PP: 871-874 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I11560789S219/19©BEIESP DOI: 10.35940/ijitee.I1156.0789S219

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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: Online shopping’s have achieved an immense growth. All like to do it as there is no need to physically to the shop and we have a wide range of collections available in the online sites from which we can actually buy the product. The customers usually tend to purchase a product that has a good customer review and has the highest rating. Numerous reviews are given for a single product and the most of the important reviews are not organized well which makes it disappear from the other reviews. Numerous researchers have worked on structuring the reviews for various purposes. In this work we propose a sentimental analysis of customer reviews for various hotel items. All the items are reviewed by the customers and the proposed work makes an analysis of the reviews obtained for a particular item in all the available shops. This analysis is helpful injudging the most likely consumed food by the customers around and can get to know the competiveness of the product being delivered to the customers. Machine Learning techniques and Natural language Processing (NLP) are used for the proposed work and is observed to produce an efficient result.

Keywords: Product Rating, Consumer review, Sentiment Classifier, Natural Language Processing Extraction, Classification, Recognition, Prediction
Scope of the Article: Software Product Lines