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Context-Enhanced Citation Sentiment Detection

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Sentiment analysis of citations in scientific papers and articles is a new and interesting problem which can open up many exciting new applications in bibliographic search and bibliometrics. Current work on citation sentiment detection focuses on only the citation sentence. In this paper, we address the problem of context-enhanced citation sentiment detection. We present a new citation sentiment corpus which has been annotated to take the dominant sentiment in the entire citation context into account. We believe that this gold standard is closer to the truth than annotation that looks only at the citation sentence itself. We then explore the effect of context windows of different lengths on the performance of a state-of-the-art citation sentiment detection system when using this context-enhanced gold standard definition.

This talk is part of the NLIP Seminar Series series.

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