IJERT-EMS
IJERT-EMS

Context Based Syntactic Opinion Mining


Context Based Syntactic Opinion Mining
Authors : Nandini S
Publication Date: 11-09-2017

Authors

Author(s):  Nandini S

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Volume. 6 - Issue. 09 , September - 2017

e-ISSN:   2278-0181

 DOI:  http://dx.doi.org/10.17577/IJERTV6IS090037

Abstract

E-commerce is evolving at such a rapid pace that new doors have been opened for users to have many opportunities to express their opinions about the product. The purpose of this project "Context Based Syntactic Opinion Mining" is to provide an effective way to view the opinions of the customers expressed in the form of customer reviews. This paper focus on aspect level opinion mining and proposes a new syntactic based approach using Natural Language Tool Kit (NLTK) and SentiWordNet. The objective of this paper is to summarize reviews of the product based on features or Aspects and classify as positive or negative opinion about a feature by assigning a score. This paper, mainly concentrates on reviews expressed about Mobile devices to extract the aspects but also applicable to other products. By analyzing the opinions of users about the features of a product, visual summary of the product can be made by plotting a graph based on score. It can also be extended to compare two mobile phone features and give an opportunity for the user to select the best among two products. This enables a user to have better understanding of the product which otherwise involves reading through long textual reviews to form a mental picture of the strengths and weaknesses of the product. This will be very useful for the customers to know about the features of product before making a buying decision. This project not only helps individuals in buying a product but also helps the organization to know how customers’ perceive their product.

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