Opinion Mining of Customer Reviews Using Supervised Learning Algorithms

Abstract

Nowadays, a lot of people express their opinions on various topics using social networking sites. Twitter has become a famous social networking site where people can express their opinions to the point and so it has become a great source for opinion mining. In this research, the goal was to train and build a model that can automatically and accurately categorize the opinion of customer tweet reviews about popular cell phone brands. We have used python TextBlob library for getting the polarity values of all the tweet reviews of the dataset. We have also used Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree and Random Forest algorithms along with Bag of Words and TF-IDF vectorizers separately to train and build the model that will categorize the customer tweet reviews into five opinion categories: Strongly Negative, Weakly Negative, Neutral, Weakly Positive and Strongly Positive. We have observed that SVM and Logistic Regression algorithms have outperformed other algorithms with 88% accuracy using Bag of Words vectorizer while SVM algorithm has outperformed other algorithms with 87% accuracy using TF-IDF vectorizer.