Emotion Intensities in Tweets

TL;DR

This paper introduces datasets and methods for detecting emotion intensity in tweets, demonstrating the impact of hashtags and establishing a benchmark system for future research.

Contribution

It creates the first annotated tweet datasets for emotion intensities and develops a benchmark regression system using best-worst scaling for annotation.

Findings

Hashtags often increase emotion intensity

Certain linguistic features are effective for intensity detection

Emotion similarities can be quantified in language

Abstract

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.