1. Context-Enhanced Citation Sentiment Analysis Awais Athar & Simone Teufel

2. Sentiment Analysis of Citations

3. Challenges in Citation Sentiment Analysis Negative sentiment is ‘politically dangerous’ - (Ziman, 1968) Personal biases are hedged - (Hyland, 1995) Criticism is ‘sweetened’ - (MacRoberts and MacRoberts, 1984; Hornsey et al., 2008) “While SCL has been successfully applied to POS tagging and Sentiment Analysis (Blitzer et al., 2006), its effectiveness for parsing was rather unexplored.”

4. Problem: Context is Ignored

5. Our Contributions A new citation sentiment corpus – contains citations annotated with the dominant sentiment in the context – closer to the truth than single-sentence citations – increases citation sentiment coverage Exploring effects of using context windows of different lengths on citation sentiment analysis

6. Corpus Construction Incoming citations to 20 papers 1,741 citations (from >800 papers) Window length of 4 4-class scheme – objective/neutral – positive – negative – e cluded

7. Annotation Unit is the Citation Problem – There may be more than 1 sentiment /citation Solution – For Gold Standard: assume last sentiment is what is really meant – For Automatic Treatment: merge citation context into one single sentence

8. Experiments SVM / 10 fold cross-validation Each citation as a feature set n-grams of length 1 to 3 Dependency triplets (Athar, 2010) det_results_The nsubj_good_results cop_good_were det_results_The nsubj_good_results cop_good_were

9. Effect of Context Size

10. Comparison with Athar (2010)

11. Conclusion Detection of citation sentiment in context around citation, not just citation sentence. New, large, context-aware citation corpus Result F=0.73 (sentiment is harder to find in science) Improvement: Use coherence features to find variable window in each document A Athar and S Teufel, "Detection of implicit citations for sentiment detection", Accepted in Workshop on Detecting Structure in Scholarly Discourse 2012, ACL 2012.

12. Thank you! Questions?