Abstract
A sentiment analysis has received a lot of attention from researchers working in the fields of natural language processing and text mining.
However, there is a lack of annotated data sets that can be used to train a model for all domains, which is hampering the accuracy of sentiment analysis.
Many research studies have attempted to tackle this issue and to improve cross-domain sentiment classification.
In this paper, we present the results of a comprehensive systematic literature review of the methods and techniques employed in a cross-domain sentiment analysis.
We focus on studies published during the period of 2010-2016.
From our analysis of those works, it is clear that there is no perfect solution.
Hence, one of the aims of this review is to create a resource in the form of an overview of the techniques, methods, and approaches that have been used to attempt to solve the problem of cross-domain sentiment analysis in order to assist researchers in developing new and more accurate techniques in the future.
https://ieeexplore.ieee.org/abstract/document/7891035
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