SemEval-2018 Task 1: Affect in Tweets

Abstract

We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification. Seventy-five teams (about 200 team members) participated in the shared task. We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful. We also analyze systems for consistent bias towards a particular race or gender. The data is made freely available to further improve our understanding of how people convey emotions through language.

An Analysis of Emotion Communication Channels in Fan-Fiction: Towards Emotional Storytelling

“…Emotion analysis has received great attention in natural language processing (Mohammad and Bravo-Marquez, 2017;Mohammad et al, 2018;Felbo et al, 2017;Abdul-Mageed and Ungar, 2017;Zhou and Wang, 2018;Gui et al, 2017, i.a.). Most existing studies on the topic cast the problem of emotion analysis as a classification task, by classifying documents (e.g., social media posts) into a set of predefined emotion classes.…”

Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis

“…mon Crawl). Each embedding is then optionally augmented with phrase and subword embeddings and fed into a CNN-LSTM model as proposed by Wu et al (2018), trained on the Affect in Tweets Dataset used at Sem Eval 2018 Task 1 (Mohammad et al, 2018). Their system achieved an average Pearson correlation score of 0.722, and ranked 12/48 in the emotion intensity regression task.…”

“…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.…”

GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus

“…Our corpora are as follows: a) A multigenre corpus created by (Tafreshi and Diab, 2018) with following genres: emotional blog posts, collected by (Aman and Szpakowicz, 2007), headlines data set from SemEval 2007-task 14 (Strapparava and Mihalcea, 2007), movie review data set (Pang and Lee, 2005) originally collected from Rotten tomatoes 6 for sentiment analysis and it is among the benchmark sets for this task. We refer to this multigenre set as (MULTI), b) SemEval-2018 Affect in Tweets data set (Mohammad et al, 2018) (AIT) with most popular emotion tags: anger, fear, joy, and sadness, c) the data set that is given for this task, which is 3-turn conversation data. From these data sets we only used the emotion tags happy, sad, and angry.…”