Sentiment Analysis of Citations using Sentence Structure-Based Features

Sentiment analysis of citations in scientific papers and articles is a new and interesting problem due to the many linguistic differences between scientific texts and other genres. In this paper, we focus on the problem of automatic identification of positive and negative sentiment polarity in citations to scientific papers. Using a newly constructed annotated citation sentiment corpus, we explore the effectiveness of existing and novel features, including n-grams, specialised science-specific lexical features, dependency relations, sentence splitting and negation features. Our results show that 3-grams and dependencies perform best in this task; they outperform the sentence splitting, science lexicon and negation based features.

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