IITP at EmoInt-2017: Measuring intensity of emotions using sentence embeddings and optimized features

dc.contributor.author	Akhtar, Md.S.
dc.contributor.author	Sawant, P.
dc.contributor.author	Ekbal, A.
dc.contributor.author	Pawar, J.D.
dc.contributor.author	Bhattacharyya, P.
dc.date.accessioned	2017-09-14T05:59:43Z
dc.date.available	2017-09-14T05:59:43Z
dc.date.issued	2017
dc.identifier.citation	Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Copenhagen, Denmark. 7–11 Sep 2017. 2017; 212-218.	en_US
dc.identifier.uri	http://www.aclweb.org/anthology/W/W17/W17-52.pdf#page=228
dc.identifier.uri	http://irgu.unigoa.ac.in/drs/handle/unigoa/4963
dc.description.abstract	This paper describes the system that we submitted as part of our participation in the shared task on Emotion Intensity (EmoInt-2017). We propose a Long short term memory (LSTM) based architecture cascaded with Support Vector Regressor (SVR) for intensity prediction. We also employ Particle Swarm Optimization (PSO) based feature selection algorithm for obtaining an optimized feature set for training and evaluation. System evaluation shows interesting results on the four emotion datasets i.e. anger, fear, joy and sadness. In comparison to the other participating teams our system was ranked 5th in the competition.	en_US
dc.publisher	Association for Computational Linguistics	en_US
dc.subject	Computer Science and Technology	en_US
dc.title	IITP at EmoInt-2017: Measuring intensity of emotions using sentence embeddings and optimized features	en_US
dc.type	Conference article	en_US