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, Sentiment 140 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 with sequential fuzzy C-means (FCM) method, and again, it is at least 7 times faster than distributed FCM method present in the 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.
Highlights
The need for opinion on a subject or product or on any particular object has a growing importance in the arena of the age of information
In this paper a method based on Discrete Fourier Transform is proposed for sentiment analysis on Temporal Intuitionistic Fuzzy set consists of membership (Positive) and nonmembership(Negative) values
The Fast Fourier Transform is used for fast calculation of DFT
Summary
The need for opinion on a subject or product or on any particular object has a growing importance in the arena of the age of information. Sentiment analysis is the key to get a fast response to have an opinion based on the comments of user and it is considered as a blooming area of research. Sentiment classification task mostly classifies the polarity with positive, negative or neutral as a binary or ternary classifier. Objective sentences refer to facts, happenings and neutral opinions whereas subjective sentences refer to positive or negative polarity values with opinion or belief or judgement. Another sub category of sentiment analysis is Granularity Oriented where opinion is generated on small or large document level, sentence level or word level basis. Methodology oriented analysis include Supervised, Semi-supervised and Unsupervised methods. Unsupervised methods handle large datasets with fast, simple and effective way
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