A study of fine-grained sentence-level emotion tagging
Gurmeet, Singh
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https://hdl.handle.net/2142/44146
Description
Title
A study of fine-grained sentence-level emotion tagging
Author(s)
Gurmeet, Singh
Issue Date
2013-05-24T21:52:35Z
Director of Research (if dissertation) or Advisor (if thesis)
Zhai, ChengXiang
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Opinion mining
Conditional random fields
Emotion tagging
Emotional profile
Abstract
While there has been much work on sentiment analysis, emotion tagging has not been very well studied. Existing work has generally treated each text article as a unit for emotion tagging. In this work, we argue that it is more useful to perform emotion tagging at the sentence-level and use Conditional Random Fields (CRF) to tag sentences with five emotion tags. We propose and study multiple features, including both basic features defined on a single sentence and dependency features defined on the context of a sentence. We create two test sets, one with email messages and one with product reviews, to evaluate the proposed features. Experimental results show that in general, dependency features are beneficial, and in particular, using relative position features can significantly improve the accuracy. We also present clustering of users based on their emotional profiles as a possible application of sentence-level emotion tagging.
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