Cross-domain sentiment classification initiated with Polarity Detection Task

Kansal, Nancy and Goel, Lipika and Gupta, Sonam (2020) Cross-domain sentiment classification initiated with Polarity Detection Task. EAI Endorsed Transactions on Scalable Information Systems, 8 (30). e1. ISSN 2032-9407

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INTRODUCTION: The requirement of the labeled dataset in the source domain makes the Cross Domain Sentiment Classification (CDSC) task complicate in the situation when the dataset is labeled manually. OBJECTIVES: To overcome the dependency of CDSC tasks on manual labeling of the dataset by proposing a polarity detection task. METHODS: We have proposed the CDSC-PDT method that is the polarity Detection Task (PDT) followed by the CDSC task. The proposed PDT task extracts the polarity of reviews from the source domain using the contextual and relevancy information of words in documents and this automatic labeled dataset is further used to train classifiers to make the further classification. RESULTS: Proposed method is comparable to the traditional learning method giving the highest precision 85.7%. CONCLUSION: The proposed method does not need to manually label the documents in either of the domain (source or target), hence it overcomes the human intervention and is also time saving and cheap process, unlike traditional CDSC tasks.

Item Type: Article
Uncontrolled Keywords: Machine Learning, Sentiment Analysis, Polarity Detection Task (PDT), Cross-Domain Sentiment Analysis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 20 Apr 2021 07:37
Last Modified: 20 Apr 2021 07:37

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