SemRec – An efficient ensemble recommender with sentiment based clustering for social media text corpus

Summary The frequent user interactions happening in the form of textual contents like reviews, ratings, tags, blogs, testimonials, and so forth transformed the social media platform into a contextualized and personalized data warehouse focusing its users' unique likes and dislikes. The huge volume of social media content makes it difficult for the end users to consume relevant information by themselves. The need of a tool to deal with such scenario leads to the development of recommendation systems. This work proposes an ensemble multi‐stage recommender system with sentiment based clustering to deal with social media text corpus where each stage performing unique functionalities of information retrieval, natural language processing, user segmentation, prediction, and recommendation generation. The proposed system leverages a hybrid approach of content‐based, collaborative, and demographic filtering techniques to predict and recommend contents, products, or services according to user interests. The experimental results gathered using standard datasets are promising and found more efficient than the traditional approaches.