IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features

Md Shad Akhtar, Palaash Sawant, Asif Ekbal, Jyoti Pawar, Pushpak Bhattacharyya


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.
Anthology ID:
W17-5229
Volume:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
WASSA | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
212–218
Language:
URL:
https://www.aclweb.org/anthology/W17-5229
DOI:
10.18653/v1/W17-5229
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-5229.pdf