Cross-domain sentiment classification via topic-related tradaboost

Xingchang Huang, Yanghui Rao*, Haoran Xie, Tak Lam Wong, Fu Lee Wang

*Corresponding author for this work

Research output: Other Conference ContributionsConference Paper (other)Researchpeer-review

16 Citations (Scopus)

Abstract

Cross-domain sentiment classification aims to tag sentiments for a target domain by labeled data from a source domain. Due to the difference between domains, the accuracy of a trained classifier may be very low. In this paper, we propose a boosting-based learning framework named TR-TrAdaBoost for cross-domain sentiment classification. We firstly explore the topic distribution of documents, and then combine it with the unigram TrAdaBoost. The topic distribution captures the domain information of documents, which is valuable for cross-domain sentiment classification. Experimental results indicate that TR-TrAdaBoost represents documents well and boost the performance and robustness of TrAdaBoost.

Original languageEnglish
Pages4939-4940
Number of pages2
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: 4 Feb 201710 Feb 2017

Conference

Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
CountryUnited States
CitySan Francisco
Period4/02/1710/02/17

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