WASSA-2017 Shared Task on Emotion Intensity

Abstract

We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet.We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best-worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.

Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus

“…One is the recent data set on for emotion intensity estimation, a shared task aiming at the development of a regression model. The goal is not to predict the emotion class, but a distribution over their intensities, and the set of emotions is limited to fear, sadness, anger, and joy (Mohammad and Bravo-Marquez, 2017).…”

Tecnolengua Lingmotif at EmoInt-2017: A lexicon-based approach

“…Unlike most shared tasks on sentiment analysis, the EmoInt Shared Task at WASSA-2017 (Mohammad andBravo-Marquez, 2017b) focused on sentiment intensity rather than classification. Several annotated Twitter datasets were provided for system training, development and testing.…”

Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis

“…Emotion detection in text includes tasks of mapping words, sentences, and documents to a discrete set of emotions following a psychological model such as those proposed by Ekman (1992) and Plutchik (1980), or to intensity scores or continuous values of valence-arousal-dominance (Posner et al, 2005). The shared task on intensity prediction for discrete classes proposed to combine both (Mohammad et al, 2018;Mohammad and Bravo-Marquez, 2017a). In this task a tweet and an emotion are given and the goal is to determine an intensity score between 0 and 1.…”

“…Distribution of the aggregated emotion scores obtained by applying the counting procedure BWS corpus or the EmoInt data(Mohammad and Bravo- Marquez, 2017b).…”