Item-Based Collaborative Filtering Using Sentiment Analysis of User Reviews

• Published in: Applications of Computing and Communication Technologies Vol. 899; pp. 77 - 87
• Main Authors: Dubey, Abhishek, Gupta, Ayush, Raturi, Nitish, Saxena, Pranshu
• Format: Book Chapter
• Language: English
• Published: Singapore Springer 01.01.2018; Springer Singapore
• Series: Communications in Computer and Information Science
• Subjects: Collaborative filtering; Item based; Logistic regression; Recommender systems; Sentiment analysis
• ISBN: 9811320349; 9789811320347
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-981-13-2035-4_8

Traditional Collaborative filtering algorithm works by using only the past experience of a user. To overcome the limitations of the traditional collaborative algorithm, an item based collaborative filtering system was introduced. In this paper, an improved recommender system is proposed. A dictionary of sentiment scores is created. These sentiment scores are calculated by finding the probability of the reviews to be positive. This sentiment score is used by an item based collaborative filtering system to improve the recommendations and filter out items with overall negative user opinion. The performance of the proposed system is compared with previous work done in this field.