Abstract
Sentiment analysis or opinion mining has an extensive area in the field of research. Today we consider the huge amount of structured and unstructured data available in the web for a particular subject to get an opinion. The surplus data handling termed as big data requires some new technology to deal with. This paper considers the requirement of sentiment analysis of such huge data for fast processing. Based on Fast Fourier Transform on Temporal Intuitionistic fuzzy set generated from text, this algorithm (FFT-TIFS) expedites the sentiment classification. Fourier analysis converts a signal from its time domain to its representation in frequency domain. Such frequency domain algorithm on Temporal Intuitionistic fuzzy set is used in Sentiment analysis for the first time. This algorithm is useful for short twitter text, document level as well as sentence level binary sentiment classification. It is tested on aclImdb, Polarity, MR, Sentiment140 and CR dataset which gives an average of 80% accuracy. The proposed method shows significant improvement in required time complexity where the method achieves 17 times faster processing in comparison to sequential Fuzzy C Means(FCM) method and again it is at least 7 times faster than distributed FCM method present in literature. The method presented in this paper has a novel approach towards fastest processing time and suitability of various sizes of the text sentiment analysis.
High performing sentiment analysis based on fast Fourier transform over temporal intuitionistic fuzzy value
