Paper: Sentiment Analysis of Citations using Sentence Structure-Based Features

ACL ID P11-3015
Title Sentiment Analysis of Citations using Sentence Structure-Based Features
Venue Annual Meeting of the Association of Computational Linguistics
Session Student Session
Year 2011
Authors

Sentiment analysis of citations in scientific pa- pers and articles is a new and interesting prob- lem due to the many linguistic differences be- tween scientific texts and other genres. In this paper, we focus on the problem of auto- matic identification of positive and negative sentiment polarity in citations to scientific pa- pers. Using a newly constructed annotated ci- tation sentiment corpus, we explore the effec- tiveness of existing and novel features, includ- ing n-grams, specialised science-specific lex- ical 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.