Product Recommendation using Sentiment Analysis of Reviews: A Random Forest Approach
Gayatri Khanvilkar1, Deepali Vora2
1Gayatri Khanvilkar, Department of Information Technology, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
2Deepali Vora, Department of Information Technology, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
Manuscript received on 10 January 2019 | Revised Manuscript received on 20 January 2019 | Manuscript Published on 30 January 2019 | PP: 146-152 | Volume-8 Issue-2S2, January 2019 | Retrieval Number: B10320182S219/19©BEIESP
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Abstract: Nowadays people are attracted to social networking sites and e-commerce websites. Due to growth in social media all the fortune companies are working on Sentiment Analysis. In Natural Language Processing, Sentiment analysis has become a major area of research. This paper explores the performance of Machine Learning Algorithms such as Multinomial Nave Bayes algorithm, Logistic Regression, SVM Classifier, Decision Tree and Random Forest are used for sentiment analysis. Comparative tabulation of above mentioned classifiers is created to analyze the performance of sentiment analysis. Random Forest can produce a great result most of the time. It is most flexible and easy to use supervised machine learning algorithm. In proposed system, Random Forest shows outstanding performance. The polarity achieved by different algorithms is used to generate product recommendations to users.
Keywords: Sentiment Analysis, Recommendation System, Machine Learning, Random Forest, Content-Based Recommendation.
Scope of the Article: Approximation and Randomized Algorithms