TY - GEN
T1 - User-guided cross-domain sentiment classification
AU - Nelakurthi, Arun Reddy
AU - Tong, Hanghang
AU - Maciejewski, Ross
AU - Bliss, Nadya
AU - He, Jingrui
N1 - Publisher Copyright:
Copyright © by SIAM.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Classification
KW - Transfer learning
KW - User modeling
UR - http://www.scopus.com/inward/record.url?scp=85027876536&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027876536&partnerID=8YFLogxK
U2 - 10.1137/1.9781611974973.53
DO - 10.1137/1.9781611974973.53
M3 - Conference contribution
AN - SCOPUS:85027876536
T3 - Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
SP - 471
EP - 479
BT - Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
A2 - Chawla, Nitesh
A2 - Wang, Wei
PB - Society for Industrial and Applied Mathematics Publications
T2 - 17th SIAM International Conference on Data Mining, SDM 2017
Y2 - 27 April 2017 through 29 April 2017
ER -