Emotion Detection in Text: Focusing on Latent Representation
This work addresses the problem of improving emotion detection accuracy for applications in marketing, psychology, and AI, but it is incremental as it builds on existing GRU-based approaches.
The paper tackled emotion detection in text by addressing limitations of conventional methods that ignore sequential and contextual aspects, and presented a bidirectional GRU network that improved performance with a 26.8-point F-measure increase on test data and 38.6 on a new dataset.
In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc. In this work, we argue that current methods which are based on conventional machine learning models cannot grasp the intricacy of emotional language by ignoring the sequential nature of the text, and the context. These methods, therefore, are not sufficient to create an applicable and generalizable emotion detection methodology. Understanding these limitations, we present a new network based on a bidirectional GRU model to show that capturing more meaningful information from text can significantly improve the performance of these models. The results show significant improvement with an average of 26.8 point increase in F-measure on our test data and 38.6 increase on the totally new dataset.