Text Analysis for Product Reviews for Sentiment Analysis using NLP Methods

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-47 Number-8
Year of Publication : 2017
Authors : S.Muthukumaran, Dr.P.Suresh
DOI :  10.14445/22315381/IJETT-V47P278


S.Muthukumaran, Dr.P.Suresh "Text Analysis for Product Reviews for Sentiment Analysis using NLP Methods", International Journal of Engineering Trends and Technology (IJETT), V47(8),474-480 May 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

This paper explains different methods for sentiment analysis and showcases an efficient methodology. It also highlights the importance the product reviews are of utmost importance for the buyers to decide based on their concerns regarding product`s various aspects for example a monitor, processor speed, memory etc. Hence this sentiment analysis of product review provides nearly accurate statistics regarding a product, providing an ease to the customers for analyzing the product and zero down his/her search for an online product. The key focus here is efficient feature extraction, polarity classification thereby summarizing positive and negative or neutral polarity. The proposed work is able to collect information from various sites and perform a sentiment analysis of a user reviews based on that information to rank a product. Also these reviews suffer from spammed reviews from unauthenticated users. In this paper, we can show that the statistical methods are often combined with traditional linguistic rules and representations. In view of these facts, we argue that the Naive Bayes classification model and Hidden Markov Models is applied to analyze the polarity of the sentiment on online product reviews due to its computational simplicity and stochastic robustness.


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Sentiment analysis (SA), Opinion mining, Machine learning, Naive Bayes (NB), Support Vector Machine (SVM), online reviews, Hidden Markov (HMM).