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        <title>IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction</title>
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        <namePart type="given">Bhaskar</namePart>
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            <title>Proceedings of the 12th International Workshop on Semantic Evaluation</title>
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            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction
%A Kotakonda, Bhaskar
%A Gowda, Prashanth
%A Lall, Brejesh
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F kotakonda-etal-2018-iit
%X 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.
%R 10.18653/v1/S18-1051
%U https://aclanthology.org/S18-1051
%U https://doi.org/10.18653/v1/S18-1051
%P 339-344

Markdown (Informal)

[IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction](https://aclanthology.org/S18-1051) (Kotakonda et al., SemEval 2018)

• IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction (Kotakonda et al., SemEval 2018)

ACL

• Bhaskar Kotakonda, Prashanth Gowda, and Brejesh Lall. 2018. IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 339–344, New Orleans, Louisiana. Association for Computational Linguistics.