Emotion Intensities in Tweets
This work addresses the need for fine-grained emotion analysis in social media, though it is incremental as it builds on existing emotion detection tasks.
The paper tackles the problem of detecting emotion intensity from text by creating the first datasets of tweets annotated for anger, fear, joy, and sadness intensities, showing that emotion-word hashtags often convey more intense emotions.
This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best--worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity, and, the extent to which two emotions are similar in terms of how they manifest in language.