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    <abstract>This paper describes our approach to the Emotion Intensity shared task. A parallel architecture of Convolutional Neural Network (CNN) and Long short term memory networks (LSTM) alongwith two sets of features are extracted which aid the network in judging emotion intensity. Experiments on different models and various features sets are described and analysis on results has also been presented.</abstract>
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%0 Conference Proceedings
%T Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets
%A Meisheri, Hardik
%A Saha, Rupsa
%A Sinha, Priyanka
%A Dey, Lipika
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y van der Goot, Erik
%S Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F meisheri-etal-2017-textmining
%X This paper describes our approach to the Emotion Intensity shared task. A parallel architecture of Convolutional Neural Network (CNN) and Long short term memory networks (LSTM) alongwith two sets of features are extracted which aid the network in judging emotion intensity. Experiments on different models and various features sets are described and analysis on results has also been presented.
%R 10.18653/v1/W17-5226
%U https://aclanthology.org/W17-5226/
%U https://doi.org/10.18653/v1/W17-5226
%P 193-199

Markdown (Informal)

[Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets](https://aclanthology.org/W17-5226/) (Meisheri et al., WASSA 2017)

• Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets (Meisheri et al., WASSA 2017)

ACL

• Hardik Meisheri, Rupsa Saha, Priyanka Sinha, and Lipika Dey. 2017. Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 193–199, Copenhagen, Denmark. Association for Computational Linguistics.