Improved emotion recognition in Spanish social media through incorporation of lexical knowledge

Abstract Emotions play an important role in human intelligence and behaviour and are a major vehicle for communication. Therefore, the integration of emotions in computational models can improve the human–computer interaction systems. In this paper, we present a study of different machine learning approaches to automatically recognise emotions in messages written in Spanish on social media. Although the computational treatment of emotion is more difficult than other sentiment analysis tasks, the baseline of some machine learning algorithms achieve an acceptable accuracy showing that it is possible to tackle the problem using some basic natural language processing techniques. In this study we have experimented with the integration of knowledge from different affective lexical resources. We conclude that the incorporation of lexical affective features leads to improvement over most baseline figures with significant improvement. Indeed, we observe that the use of resources generated particularly for emotion recognition in other languages than English is a promising approach to enhance basic machine learning systems. Particularly, we used a Spanish lexical resource and we notice that it always improves the results. In the best case, it improves 6.15% of the results obtained using the Naive Bayes classifier.