Feature-based mobile phone rating using sentiment analysis and machine learning approaches

Abstract

This paper proposes a model of sentiment analysis of various features of different companies' mobile phones and their overall rating. Before buying a phone, customers usually look for reviews to decide which phone to buy. The model proposed in this paper provides an optimal solution for the customer for making this decision more efficiently. In this model, each feature of a mobile phone is rated based on public opinion and an overall rating for each phone is provided. Amazon is one of the largest Internet retailers, which makes way for most public reviews on their products. These reviews are collected as a form of an open source platform and used as the dataset in this model. The gathered data is preprocessed and then separated into two different sets - Training Set and Testing Set which are used to train and test the supervised machine learning algorithms for classification. 15 most common features of the mobile phones based on public reviews are selected from the training data set and used as the feature set in this model. Different algorithms which include Naïve Bayes, Support Vector Machine, Logistic Regression, and Stochastic Gradient Descent algorithms are used in this model and the comparison of their performance is shown. This model provides a rating of each feature and an average rating of the mobile phone based on sentiment polarity. Thus, this research work can assist potential customers to choose the best product based on the opinion of the other users.