IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction

Bhaskar Kotakonda, Prashanth Gowda, Brejesh Lall


Abstract
This paper discusses the experiments performed for predicting the emotion intensity in tweets using a generalized supervised learning approach. We extract 3 kind of features from each of the tweets - one denoting the sentiment and emotion metrics obtained from different sentiment lexicons, one denoting the semantic representation of the word using dense representations like Glove, Word2vec and finally the syntactic information through POS N-grams, Word clusters, etc. We provide a comparative analysis of the significance of each of these features individually and in combination tested over standard regressors avaliable in scikit-learn. We apply an ensemble of these models to choose the best combination over cross validation.
Anthology ID:
S18-1051
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
339–344
Language:
URL:
https://www.aclweb.org/anthology/S18-1051
DOI:
10.18653/v1/S18-1051
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1051.pdf