Abstract
The opinion seeking behavior of people for good decision making has greatly enhanced the importance of social media as a platform for exchange of information.
This trend has led to a sudden spurt of information overflow on the Web.
The huge volume of such information has to be technically processed for segregating the relevant knowledge. Sentiment analysis is the popular method extensively used for this purpose.
It is defined as the computational study of mining the opinions from the available content about the entity of interest.
Existing Sentiment analysis techniques quite efficiently capture opinions from text written in syntactically correct and explicit language.
However, while dealing with the informal data, limitation has been observed in performance of sentiment analysis techniques.
With a view to deal with the imperfect and indirect language used by the netizens, it has become necessary to work on improvement in the existing sentiment analysis techniques.
In this regard, the conventional sentiment analysis techniques have shown some improvement on applying the appropriate context information.
However, still there is ample scope for further research to find the relevant “context” and applying it to a given scenario.
This systematic literature review paper intends to explore and analyze the existing work on the context-based sentiment analysis and to report gaps and future directions in the said research area.
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https://link.springer.com/article/10.1007%2Fs11042-019-7346-5
