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Approaches to Cross-Domain Sentiment Analysis: A Systematic Literature Review
- Source :
- IEEE Access, Vol 5, Pp 16173-16192 (2017)
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
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.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 5
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.32b49eaa094f4fec811606955ddbb622
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2017.2690342