Automatic Product Saleability Prediction using Sentiment Analysis on User Reviews

Abstract

From past few decades information technology industry is on the rise and software development companies thrive to provide the best results for the consumers. Sentiment Analysis is a powerful tool that can help the software industry and company to better evaluate user needs and cater the software in a way to maximise the sales potential. Sentiment Analysis combined with machine learning techniques can help us learn about the industrial trends. Greater than 40 thousand Exabyte (10^18) of data is estimated to be a part of the internet out of which 80% is unstructured and can be processed to useful means using NLP techniques. In proposed work sentiment analysis has been applied on user review to predict its saleability or in simpler words: How well a product will sell? Customer feedback was collected from users through a feedback form which required them to express their satisfaction with the product by answering a set of questions which serves as features and input to the machine which evaluates the features such as user interface, Performance, feasibility, cost effectiveness and customer service by extracting the polarity from each. The result shows that sentiment analysis is a viable option to predict the saleability of a product. The empirical results are close to the customer’s own expected probability of buying.