International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 181 - Number 37 |
Year of Publication: 2019 |
Authors: Parvati Kadli, Vidyavathi B. M. |
10.5120/ijca2019918338 |
Parvati Kadli, Vidyavathi B. M. . Cross Domain Sentiment Classification Techniques: A Review. International Journal of Computer Applications. 181, 37 ( Jan 2019), 13-20. DOI=10.5120/ijca2019918338
With the explosive growth in the availability of online resources, sentiment analysis has become an interesting topic for researchers working in the field of natural language processing and text mining. The social media corpus can span many different domains. It is difficult to get annotated data of all domains that can be used to train a learning model. Hence continuous efforts are made to tackle the issue and many techniques have been designed to improve cross domain sentiment analysis. In this paper we present literature review of methods and techniques employed for cross domain sentiment analysis. The aim of the review is to present an overview of techniques and approaches, datasets used to solve cross domain sentiment classification problem in the research work carried out in the recent years.