Abstract
The study of affective language has had numerous developments in the Natural Language Processing area in recent years, but the focus has been predominantly on Sentiment Analysis, an expression usually used to refer to the classification of texts according to their polarity or valence (positive vs. negative). The study of emotions, such as joy, sadness, anger, surprise, among others, has been much less developed and has fewer resources, both for English and for other languages, such as Spanish. In this paper, we present the most relevant existing resources for the study of emotions, mainly for Spanish; we describe some heuristics for the union of two existing corpora of Spanish tweets; and based on some experiments for classification of tweets according to seven categories (anger, disgust, fear, joy, sadness, surprise, and others) we analyze the most problematic classes.
Highlights
The study of sentiments and emotions expressed in texts has been part of the NaturalLanguage Processing field since the 1990s,increasing in interest in the last two decades due to the large amount of texts available on the Internet [1], especially messages on social networks where large numbers of people give their opinion on all relevant topics and express their personal emotions
Automatic emotion classification has not been as extensively investigated as polarity classification, which is the most commonly addressed problem when talking about sentiment analysis
We worked on the expansion of the EmoEvent corpus, which was used in the TASS2020 and EmoEvalEs-2021 tasks, by adapting the corpus used in the subtask on emotion classification (E-c) at SemEval-2018 Task1
Summary
The study of sentiments and emotions expressed in texts has been part of the Natural. Language Processing field since the 1990s,increasing in interest in the last two decades due to the large amount of texts available on the Internet [1], especially messages on social networks where large numbers of people give their opinion on all relevant topics and express their personal emotions Within this area, work has been done mainly on the classification of texts by their polarity or valence (positive or negative, differentiating in general neutral texts), this problem is usually called Sentiment Analysis. Emotion classification requires more expensive resources than the usual ones for polarity classification, as it requires more examples to cover the full set of classes, more annotators, and more attention to differentiate a varied number of classes and achieve a reliable inter-annotator agreement This effort is worthwhile as it could help in the detection of different problems that people experience and frequently express in social networks.
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