Multidimensional sentiment analysis on twitter with semiotics

The purpose of social media websites like Twitter, Tumbler, and Facebook is that its user can express their feelings without being pressurized by anyone. User can give their point of view regarding the recent events in their surroundings as well as give suggestions to improve surroundings in text-based format while conveying their emotions which they are not able to easily verbalize using emoticons and emoji. For better understanding of people’s opinion, it is important to analyze this semiotics as well as sentence. In this paper we will discuss importance of semiotics in sentiment analysis. The main contribution of this paper to provide an approach to determine sentiment score of a tweet with semiotics with multi-dimensional sentiment analysis. In our algorithmic approach we have created semiotic dictionary which have sentiment score for each semiotic with sentiment expressed by it most frequently. We have compared our algorithmic approach with the prediction approach for sentiment classification and calculating sentiment scores. Proposed approach overcome limitation of regression analysis approach as it also helps finding sentiment score in case of where semiotic role is “Addition” and it is more effective at calculating sentiment score than other approach.