期刊
IEEE ACCESS
卷 5, 期 -, 页码 16173-16192出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2690342
关键词
Cross-domain sentiment analysis; domain adaptation for sentiment analysis; multi-domain sentiment analysis; sentiment analysis; systematic literature review; transfer learning
资金
- Ministry of Higher Education, Malaysia [FRGS/1/2016/ICT02/UKM/02/11, FRGS/1/2015/ICT02/UKM/01/2]
- Universiti Kebangsaan Malaysia [DIP-2016-024]
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.
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