Abstract
The paper describes experiments on estimating emotion intensity in tweets using a generalized regressor system. The system combines lexical, syntactic and pretrained word embedding features, trains them on general regressors and finally combines the best performing models to create an ensemble. The proposed system stood 3rd out of 22 systems in the leaderboard of WASSA-2017 Shared Task on Emotion Intensity.
Cite
CITATION STYLE
Duppada, V., & Hiray, S. (2017). Seernet at EmoInt-2017: Tweet emotion intensity estimator. In EMNLP 2017 - 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2017 - Proceedings of the Workshop (pp. 205–211). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-5228
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