UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish

Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), pages 279–285 New Orleans, Louisiana, June 5–6, 2018. ©2018 Association for Computational Linguistics UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish Marloes Kuijper CLCG University of Groningen marloes.madelon @gmail.com Mike van Lenthe CLCG University of Groningen mikevanlenthe @gmail.com Rik van Noord CLCG University of Groningen r.i.k.van.noord@rug.nl Abstract 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 train- ing 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 en- semble of different models as opposed to sim- ply averaging did not result in an increase in performance. We placed second (EI-Reg), sec- ond (EI-Oc), fourth (V-Reg) and fifth (V-Oc) in the four Spanish subtasks we participated in. 1 Introduction Understanding the emotions expressed in a text or message is of high relevance nowadays. Compa- nies are interested in this to get an understanding of the sentiment of their current customers regard- ing their products and the sentiment of their po- tential customers to attract new ones. Moreover, changes in a product or a company may also af- fect the sentiment of a customer. However, the intensity of an emotion is crucial in determining the urgency and importance of that sentiment. If someone is only slightly happy about a product, is a customer willing to buy it again? Conversely, if someone is very angry about customer service, his or her complaint might be given priority over somewhat milder complaints. Mohammad et al. (2018) present four tasks1 in which systems have to automatically determine the intensity of emotions (EI) or the intensity of the sentiment (Valence) of tweets in the languages English, Arabic, and Spanish. The goal is to ei- ther predict a continuous regression (reg) value or 1We did not participate in subtask 5 (E-c). to do ordinal classification (oc) based on a num- ber of predefined categories. The EI tasks have separate training sets for four different emotions: anger, fear, joy and sadness. Due to the large num- ber of subtasks and the fact that this language does not have many resources readily available, we only focus on the Spanish subtasks. Our work makes the following contributions: • We show that automatically translating En- glish lexicons and English training data boosts performance; • We show that employing semi-supervised learning is beneficial; • We show that the stepwise creation of an ensemble model is not necessarily better method than simply averaging predictions. Our submissions ranked second (EI-Reg), sec- ond (EI-Oc), fourth (V-Reg) and fifth (V-Oc), demonstrating that the proposed method is accu- rate in automatically determining the intensity of emotions and sentiment of Spanish tweets. This paper will first focus on the datasets, the data gen- eration procedure, …

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