Sentiment extraction and classification for the analysis of users’ interest in tweets

This paper aims to propose an approach for the analysis of user interest based on tweets, which can be used in the design of user recommendation systems. The extract topics are seen positively by the user. Design/methodology/approach: The proposed approach is based on the combination of sentiment extraction and classification analysis of tweet to extract the topic of interest. The proposed hybrid method is original. The topic extraction phase uses a method based on semantic distance in the WordNet taxonomy. Sentiment extraction uses NLPcore. Findings: The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results and confirm the suitability of the approach combining sentiment and categorization for the topic of interest extraction. Research limitations/implications: The hybrid method combining sentiment extraction and classification for user positive topics represents a novel contribution with many potential applications. Practical implications: The functionality of positive topic extraction is very useful as a component in the design of a recommender system based on user profiling from Twitter user behaviors. Social implications: The application of the proposed method in short-text social network can be massive and beyond the applications in tweets. Originality/value: There are few works that have considered both sentiment analysis and classification to find out users’ interest. The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results.