@InProceedings{akhtar-EtAl:2017:WASSA2017,
  author    = {Akhtar, Md Shad  and  Sawant, Palaash  and  Ekbal, Asif  and  Pawar, Jyoti  and  Bhattacharyya, Pushpak},
  title     = {IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features},
  booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {212--218},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-5229}
}