Dual Sentimental Analysis On Product Reviews
Journal Title: International Journal of Engineering and Science Invention - Year 2018, Vol 7, Issue 10
Abstract
Online reviews available on the internet are increased in large amount in recent years. Sentiment analysis is a natural language processing that predict the mood of the public. It have a major role in marketing intelligence to analyses, visualize and predict the relations between products and customer reviews. It helps marketing enterprises to find the potential risks and to design new products and marketing strategies. Using this a customer can easily see how the existing customers feel about a product and product manufacturer can find what the customers thought about their product by investigating reviews, tweets, blogs etc. In Sentiment classification BOW model is the generally used text representation model that follow the techniques of traditional topic based text classification. The performance of BOW sometimes remains limited due to some fundamental deficiencies in handling the polarity shift problem. Due to polarity shift problem, accuracy of finding the sentiment of a statement decreases. Dual sentimental analysis is done by taking original and reversed training reviews for sentiment classification to avoid the shift polarity problem and to improve the accuracy to predict the sentiments. Dual sentimental analysis will predict the sentiment of product reviews efficiently. Marketing industries can use this technique to predict the costumer responses and their opinions regarding their product. This paper presents the techniques and methods in Dual sentiment analysis and how it can be used to predict the sentiment of product reviews.
Authors and Affiliations
Shilpa A B, Jisha G
Keywords
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