Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Building emotion lexicon from weblog corpora
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UA-ZBSA: a headline emotion classification through web information
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
From once upon a time to happily ever after: tracking emotions in novels and fairy tales
LaTeCH '11 Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
Mining sentiment terminology through time
Proceedings of the 21st ACM international conference on Information and knowledge management
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We present an approach to automatically generate a word-emotion lexicon based on a smaller human-annotated lexicon. To identify associated feelings of a target word (a word being considered for inclusion in the lexicon), our proposed approach uses the frequencies, counts or unique words around it within the trigrams from the Google n-gram corpus. The approach was tuned using as training lexicon, a subset of the National Research Council of Canada (NRC) word-emotion association lexicon, and applied to generate new lexicons of 18,000 words. We present six different lexicons generated by different ways using the frequencies, counts, or unique words extracted from the n-gram corpus. Finally, we evaluate our approach by testing each generated lexicon against a human-annotated lexicon to classify feelings from affective text, and demonstrate that the larger generated lexicons perform better than the human-annotated one.