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EMNLP
2008

Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis

10 years 5 months ago
Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis
Determining the polarity of a sentimentbearing expression requires more than a simple bag-of-words approach. In particular, words or constituents within the expression can interact with each other to yield a particular overall polarity. In this paper, we view such subsentential interactions in light of compositional semantics, and present a novel learningbased approach that incorporates structural inference motivated by compositional semantics into the learning procedure. Our experiments show that (1) simple heuristics based on compositional semantics can perform better than learning-based methods that do not incorporate compositional semantics (accuracy of 89.7% vs. 89.1%), but (2) a method that integrates compositional semantics into learning performs better than all other alternatives (90.7%). We also find that "contentword negators", not widely employed in previous work, play an important role in determining expression-level polarity. Finally, in contrast to conventional...
Yejin Choi, Claire Cardie
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where EMNLP
Authors Yejin Choi, Claire Cardie
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