Sentiment analysis of tweets using refined neutrosophic sets

•Proposes a new concept called Multi Refined Neutrosophic Sets (MRNS), which has seven memberships namely strong positive, positive, positive indeterminate, indeterminate, negative indeterminate, negative and strong negative.•The properties of MRNS are discussed and operators over MRNS are defined.•For sentiment analysis of tweets ten case studies were considered. In each case study, 1000 tweets were extracted using twitter API. Python libraries were used for sentiment analysis.•For the first time neutrosophic sets have been used for sentiment analysis of tweets.•Single valued neutrosophic set (SVNS) and Triple refined indeterminate neutrosophic set (TRINS) were used for sentiment analysis.•The newly proposed MRNS is also used for sentiment analysis of all the 10 cases.•The comparative study of the sentiment analysis carried out with the three methods (SVNS, TRINS and MRNS) is tabulated.•This method using MRNS is more efficient in capturing the opinion of the tweets with best accuracy. In the last decade, opinion mining and sentiment analysis have been the subject of fascinating interdisciplinary research. Alongside the evolution of social media networks, the sheer volume of social media text available for sentiment analysis has increased multi-fold, leading to a formidable corpus. Sentiment analysis of tweets have been carried out to gauge public opinion on breaking news, various policies, legislations, personalities and social movements. Fuzzy logic has been used in the sentiment analysis of twitter data, whereas neutrosophy which factors in the concept of indeterminacy has not been used to analyse tweets. In this paper, the concept of multi refined neutrosophic set (MRNS) with two positive, three indeterminate and two negative memberships is proposed. Single valued neutrosophic set (SVNS), triple refined indeterminate neutrosophic set (TRINS) and MRNS have been used in the sentiment analysis of tweets on ten different topics. Eight of these topics chosen for sentiment analysis are related to Indian scenario and two topics to international scenario. A comparative analysis of the methods show that the approach with MRNS provides better refinement to the indeterminacy present in the data.