ABSTRACT
Sentiment detection and classification is the latest fad for social analytics on Web.
With the array of practical applications in healthcare, finance, media, consumer markets, and government, distilling the voice of public to gain insight to target information and reviews is non-trivial.
With a marked increase in the size, subjectivity, and diversity of social web-data, the vagueness, uncertainty and imprecision within the information has increased manifold.
Soft computing techniques have been used to handle this fuzziness in practical applications.
This work is a study to understand the feasibility, scope and relevance of this alliance of using Soft computing techniques for sentiment analysis on Twitter.
We present a systematic literature review to collate, explore, understand and analyze the efforts and trends in a well-structured manner to identify research gaps defining the future prospects of this coupling.
The contribution of this paper is significant because firstly the primary focus is to study and evaluate the use of soft computing techniques for sentiment analysis on Twitter and secondly as compared to the previous reviews we adopt a systematic approach to identify, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high-quality studies to address specific research questions pertaining to the defined research domain.
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https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5107
