1,144 research outputs found
SemEval-2018 Task 1: Affect in Tweets
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
UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish
The present study describes our submission to SemEval 2018 Task 1: Affect in
Tweets. Our Spanish-only approach aimed to demonstrate that it is beneficial to
automatically generate additional training data by (i) translating training
data from other languages and (ii) applying a semi-supervised learning method.
We find strong support for both approaches, with those models outperforming our
regular models in all subtasks. However, creating a stepwise ensemble of
different models as opposed to simply averaging did not result in an increase
in performance. We placed second (EI-Reg), second (EI-Oc), fourth (V-Reg) and
fifth (V-Oc) in the four Spanish subtasks we participated in.Comment: Accepted at SemEval 201
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