EmoIntens Tracker at SemEval-2018 Task 1: Emotional Intensity Levels in #Tweets

Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), pages 177–180 New Orleans, Louisiana, June 5–6, 2018. ©2018 Association for Computational Linguistics EmoIntens Tracker at SemEval-2018 Task 1: Emotional Intensity Levels in #Tweets Ramona-Andreea Turcu1, Sandra Maria Amarandei1, Iuliana-Alexandra Flescan-Lovin-Arseni1, Daniela Gifu1,2, Diana Trandabat1 1Faculty of Computer Science, “Alexandru Ioan Cuza” University of Iasi, 2Institute of Computer Science, Romanian Academy - Iasi Branch {ramona.turcu, daniela.gifu, dtrandabat}@info.uaic.ro {maria.amarandei, flescan.alexandra}@gmail.com 1 Introduction Emotion Analysis is still a challenging task in NLP (Natural Language Processing); the researchers try to recognize not only emotions “generally speaking” but also their intensity. Because the level of subjectivity is particularly high in this matter, prediction of emotions, mainly in text, demands for continuous research and improvement strategies. SemEval competition has already a tradition in developing tasks to address this subject. This year proposed an even more challenging task: emotion intensity prediction in tweets. Within this context, this present study aims to develop a system that can not only detect emotions but also their intensities, namely emotion intensity regression and emotion intensity ordinal classification tasks for fear, joy, sadness and anger. Better results are finally provided thanks to the combination between neural network and proper decision tree algorithms. From the four tasks, namely: EI-reg, EI-oc, VAD-reg, and VAD-oc, the system focuses on the first two of them, for both English and Spanish datasets, according to their relation with the emotion concept. While tweets annotation, emotion intensity regression and emotion intensity ordinal classification is an active field of emotion analysis, we believe that a supervised machine learning (Naive Bayes, KNN and SVM), deep learning approach (NN Tensor Flow model), and decision trees would increase the effectiveness of this system. 2 State of the Art Some of the most used technologies which led to considerable results and step forwards in the domain of sentiment analysis are mainly represented by machine learning algorithms which already have led to impressive results can be thoroughly analyzed in several studies such as: the work of Bac Huy Nguyen (2015); latent semantic analysis (LSA) by Andrew et al. (2014), support vector machines in the work of Rohini S. Rahate and Emmanuel M, (2013), grammatical dependency relations, Support Vector Regression (SVR), and Neural Networks. Abstract The „Affect in Tweets” task is centered on emotions categorization and evaluation matrix using multi-language tweets (English and Spanish). In this research, SemEval Affect dataset was preprocessed, categorized, and evaluated accordingly (precision, recall, and accuracy). The system described in this paper is based on the implementation of supervised machine learning (Naive Bayes, KNN and SVM), deep learning (NN Tensor Flow model), and decision trees algorithms. 177 An important work has been done by the tool IZU-NLP at EmoInt-2017 (Yuanye He et al., 2017), meant to determine numerical values that would represent the emotion intensity in a tweet. There are researchers who prefer to combine several methods in order to achieve better results. For instance, in the paper of Sreekanth Maunendra and Sankar Desarkar (2017) there were implemented three regression methods: …

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