Context-EnhancedCitation Sentiment Analysis

Awais Athar & Simone Teufel

Sentiment Analysis of Citations

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.”

Problem: Context is Ignored

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

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 cludedx

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

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_Thensubj_good_resultscop_good_were

Effect of Context Size

𝑙 𝑟

Comparison with Athar (2010)

• M• At the cost of slight decrease in (0.77 0.73)

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.

Thank you!

Questions?