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

• Md Shad Akhtar ^[2] ; Palaash Sawant ^[1] ; Asif Ekbal ^[2] ; Jyoti Pawar ^[1] ; Pushpak Bhattacharyya ^[2]
  1. [1] Goa University

     Goa University

     India

     
  2. [2] Indian Institute of Technology Patna, India
• Localización: 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis WASSA 2017: Proceedings of the Workshop / Alexandra Balahur Dobrescu (ed. lit.), Saif M. Mohammad (ed. lit.), Erik van der Goot (ed. lit.), 2017, ISBN 978-1-945626-95-1, págs. 212-218
• Idioma: inglés
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• Resumen

  • This paper describes the system that we submitted as part of our participation in the shared task on Emotion Inten- sity (EmoInt-2017). We propose a Long short term memory (LSTM) based archi- tecture cascaded with Support Vector Re- gressor (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 emo- tion datasets i.e. anger, fear, joy and sad- ness. In comparison to the other partici- pating teams our system was ranked 5th in the competition.