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PROJECT TITLE :

Approaches to Cross-Domain Sentiment Analysis: A Systematic Literature Review - 2017

ABSTRACT:

A sentiment analysis has received a ton of attention from researchers operating within the fields of natural language processing and text mining. However, there's a scarcity of annotated information sets which will be used to coach a model for all domains, which is hampering the accuracy of sentiment analysis. Many research studies have tried to tackle this issue and to improve cross-domain sentiment classification. During this paper, we tend to gift the results of a comprehensive systematic literature review of the ways and techniques utilized in a cross-domain sentiment analysis. We have a tendency to specialize in studies printed during the period of 2010-2016. From our analysis of these works, it is clear that there is no good solution. Hence, one in all the aims of this review is to create a resource in the shape of an outline of the techniques, methods, and approaches that are used to try to solve the problem of cross-domain sentiment analysis in order to help researchers in developing new and additional accurate techniques in the future.


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