A Literature Review on Cross Domain Sentiment Analysis Using Machine learning

A Literature Review on Cross Domain Sentiment Analysis Using Machine learning

Nancy Kansal, Lipika Goel, Sonam Gupta
Copyright: © 2020 |Pages: 14
DOI: 10.4018/IJAIML.2020070103
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Abstract

Sentiment analysis is the field of NLP which analyzes the sentiments of text written by users on online sites in the form of reviews. These reviews may be either in the form of a word, sentence, document, or ratings. These reviews are used as datasets when applied to train a classifier. These datasets are applied in the annotated form with the positive, negative or neutral labels as an input to train the classifier. This trained classifier is used to test other reviews, either in the same or different domains to know like or dislike of the user for the related field. Various researches have been done in single and cross domain sentiment analysis. The new methods proposed are overcoming the previous ones but according to this survey, no methods best suit the proposed work. In this article, the authors review the methods and techniques that are given by various researchers in cross domain sentiment analysis and how those are compared with the pre-existing methods for the related work.
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2. Key Terminology

Here, the authors define some basic terminologies that are used for this review purpose.

2.1 Domain

With respect to this research, the domain is such collection where all the entities have similar characteristics like in electronics products, DVD is one domain and AC is a different domain. In social networking sites, Twitter is one domain and Facebook is a different domain.

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