Towards Affect-based User Models: a comparative study with various datasets, features and algorithms, for multi-labeled probabilistic affect detection



In this paper, we use machine learning techniques to try to find the best possible combination of datasets, affect lexicons, features, and algorithms, for the automatic detection of affect in text. We consider as affective categories, the six basic emotions from Paul Ekman (anger, disgust, fear, happiness, sadness, and surprise). For the experiments, we count with three different datasets (news headlines, fairy tales, and blogs sentences), and two affect lexicons: WordNet-Affect and Roget's Thesaurus. From this collection of data, we compare the performance of two classification algorithms: Naive Bayes and Support Vector Machines (SVM). The results demonstrate that there are two combinations, in particular, that are more appropriate for the purpose under study. We evaluate and discuss the results from the perspective that in the future, we intend to build user affect models, based on the emotional information that can be collected from sentences, or texts, of the users.


Machine Learning, Sentiment Analysis, Emotion Detection, Text Classification


Machine Learning


5th Symposium on Informatics (INForum 2013), Évora, Portugal 2013

Cited by

Year 2017 : 1 citations

 Aballay, Laura, Silvana Aciar, and Eliseo Reategui. "Método para detección de emociones desde foros utilizando Text Mining." Campus Virtuales 6.1 (2017): 89-98.

Year 2015 : 1 citations

 Aballay, Laura N., Silvana Aciar, and Eliseo Berni Reategui. "Propuesta de un Método para Detección de Emociones en E-Learning." Argentine Symposium on Artificial Intelligence (ASAI 2015)-JAIIO 44 (Rosario, 2015). 2015.