A Deep Neural Network Model for Cross-Domain Sentiment Analysis

Suman Kumari, Basant Agarwal and Mamta Mittal
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Suman Kumari: Swami Keshvanand Institute of Technology, Management, and Gramothan, Jaipur, India
Basant Agarwal: Indian Institute of Information Technology, Kota, India
Mamta Mittal: G.B. Pant Government Engineering College, New Delhi, India

International Journal of Information System Modeling and Design (IJISMD), 2021, vol. 12, issue 2, 1-16

Abstract: Sentiment analysis is used to detect the opinion/sentiment expressed from the unstructured text. Most of the existing state-of-the-art methods are based on supervised learning, and therefore, a labelled dataset is required to build the model, and it is very difficult task to obtain a labelled dataset for every domain. Cross-domain sentiment analysis is to develop a model which is trained on labelled dataset of one domain, and the performance is evaluated on another domain. The performance of such cross-domain sentiment analysis is still very limited due to presence of many domain-related terms, and the sentiment analysis is a domain-dependent problem in which words changes their polarity depending upon the domain. In addition, cross-domain sentiment analysis model suffers with the problem of large number of out-of-the-vocabulary (unseen words) words. In this paper, the authors propose a deep learning-based approach for cross-domain sentiment analysis. Experimental results show that the proposed approach improves the performance on the benchmark dataset.

Date: 2021
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