User-guided cross-domain sentiment classification

Arun Reddy Nelakurthi, Hanghang Tong, Ross MacIejewski, Nadya Bliss, Jingrui He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sentiment analysis has been studied for decades, and it is widely used in many real applications such as media monitoring. In sentiment analysis, when addressing the problem of limited labeled data from the target domain, transfer learning, or domain adaptation, has been successfully applied, which borrows information from a relevant source domain with abundant labeled data to improve the prediction performance in the target domain. The key to transfer learning is how to model the relatedness among different domains. For sentiment analysis, a common practice is to assume similar sentiment polarity for the common keywords shared by different domains. However, existing methods largely overlooked the human factor, i.e., the users who expressed such sentiment. In this paper, we address this problem by explicitly modeling the human factor related to sentiment classification. In particular, we assume that the content generated by the same user across different domains is biased in the same way in terms of the sentiment polarity. In other words, optimistic/pessimistic users demonstrate consistent sentiment patterns, no matter what the context is. To this end, we propose a new graph-based approach named U-Cross, which models the relatedness of different domains via both the shared users and keywords. It is non-parametric and semi-supervised in nature. Furthermore, we also study the problem of shared user selection to prevent 'negative transfer'. In the experiments, we demonstrate the effectiveness of U-Cross by comparing it with existing state-of-the-art techniques on three real data sets.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
EditorsNitesh Chawla, Wei Wang
PublisherSociety for Industrial and Applied Mathematics Publications
Pages471-479
Number of pages9
ISBN (Electronic)9781611974874
DOIs
StatePublished - 2017
Event17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States
Duration: Apr 27 2017Apr 29 2017

Publication series

NameProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017

Other

Other17th SIAM International Conference on Data Mining, SDM 2017
CountryUnited States
CityHouston
Period4/27/174/29/17

Keywords

  • Classification
  • Transfer learning
  • User modeling

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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