EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
This work addresses emotion analysis for natural language processing applications, but it is incremental as it builds on existing RNN and attention methods.
The paper tackled emotion intensity prediction by introducing a deep learning system using inner attention on an RNN to identify emotion-bearing words without lexicons, achieving 13th place out of 22 competitors in the WASSA 2017 shared task.
In this paper we describe a deep learning system that has been designed and built for the WASSA 2017 Emotion Intensity Shared Task. We introduce a representation learning approach based on inner attention on top of an RNN. Results show that our model offers good capabilities and is able to successfully identify emotion-bearing words to predict intensity without leveraging on lexicons, obtaining the 13th place among 22 shared task competitors.