Sentiment Analysis from Social Media Tweets Using Single-Valued Neutrosophic Sets and Fuzzy Sets

In the last ten years, exciting work at the intersection of several academic disciplines has been done in the areas of view mining and sentiment analysis. The sheer amount of social media text that is now accessible for sentiment analysis has expanded by a factor of multiples with the development of social media networks, resulting in the creation of a formidable corpus. An examination of the sentiments included within tweets has been performed to measure the general public's perspective on breaking news, as well as a variety of laws, regulations, individuals, and political movements. In the assessment of the sentiment of Twitter data, fuzzy logic (FL) was used, but neutrosophy, which makes consideration the idea of indeterminacy, was not applied. Fuzzy logic (FL) was utilized since neutrosophy was not utilized to analyze tweets. In this study, we present the idea of single valued-neutrosophic sets (SVNSs) that may have positive, indeterminate, and negative memberships. We used the sanders dataset to apply the proposed methodology. The fuzzy set (FS) has the indeterminacy value opposite the NS. FS has two only degrees, truth, and falsity. This paper shows the difference between the NS and FS in the sample of data.