NDLI: Cross-domain and cross-category emotion tagging for comments of online news

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Cross-domain and cross-category emotion tagging for comments of online news

Content Provider	ACM Digital Library
Author	Zhang, Ning Lu, Yanshan Wang, Qifan Zhang, Ying Si, Luo Yuan, Xiaojie
Abstract	In many online news services, users often write comments towards news in subjective emotions such as sadness, happiness or anger. Knowing such emotions can help understand the preferences and perspectives of individual users, and therefore may facilitate online publishers to provide more relevant services to users. Although building emotion classifiers is a practical task, it highly depends on sufficient training data that is not easy to be collected directly and the manually labeling work of comments can be quite labor intensive. Also, online news has different domains, which makes the problem even harder as different word distributions of the domains require different classifiers with corresponding distinct training data. This paper addresses the task of emotion tagging for comments of cross-domain online news. The cross-domain task is formulated as a transfer learning problem which utilizes a small amount of labeled data from a target news domain and abundant labeled data from a different source domain. This paper proposes a novel framework to transfer knowledge across different news domains. More specifically, different approaches have been proposed when the two domains share the same set of emotion categories or use different categories. An extensive set of experimental results on four datasets from popular online news services demonstrates the effectiveness of our proposed models in cross-domain emotion tagging for comments of online news in both the scenarios of sharing the same emotion categories or having different categories in the source and target domains.
Starting Page	627
Ending Page	636
Page Count	10
File Format	PDF
ISBN	9781450322577
DOI	10.1145/2600428.2609587
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cross-category Transfer learning Comments Online news Sentiment tagging Cross-domain
Content Type	Text
Resource Type	Article

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