The rise of social networks has allowed misogynistic, xenophobic, and homophobic people to spread their hate-speech to intimidate individuals or groups because of their gender, ethnicity or sexual orientation. The consequences of hate-speech are devastating, causing severe depression and even leading people to commit suicide. Hate-speech identification is challenging as the large amount of daily publications makes it impossible to review every comment by hand. Moreover, hate-speech is also spread by hoaxes that requires language and context understanding. With the aim of reducing the number of comments that should be reviewed by experts, or even for the development of autonomous systems, the automatic identification of hate-speech has gained academic relevance. However, the reliability of automatic approaches is still limited specifically in languages other than English, in which some of the state-of-the-art techniques have not been analyzed in detail. In this work, we examine which features are most effective in identifying hate-speech in Spanish and how these features can be combined to develop more accurate systems. In addition, we characterize the language present in each type of hate-speech by means of explainable linguistic features and compare our results with state-of-the-art approaches. Our research indicates that combining linguistic features and transformers by means of knowledge integration outperforms current solutions regarding hate-speech identification in Spanish.

Polarity classification is a well-known Sentiment Analysis task. However, most research has been oriented towards developing supervised or unsupervised systems without paying much attention to certain linguistic phenomena such as negation. In this paper we focus on this specific issue in order to demonstrate that dealing with negation can improve the final system. Although we can find some studies of negation detection, most of them deal with English documents. On the contrary, our study is focused on the scope of negation in Spanish Sentiment Analysis. Thus, we have built an unsupervised polarity classification system based on integrating external knowledge. In order to evaluate the influence of negation we have implemented a specific module for negation detection by applying several rules. The system has been tested considering and without considering negation, using a corpus of tweets written in Spanish. The results obtained reveal that the treatment of negation can greatly improve the accuracy of the final system. Moreover, we have carried out a comprehensive statistical study in order to demonstrate our approach. To the best of our knowledge, this is the first work which statistically demonstrates that taking into account negation significantly improves the polarity classification of Spanish tweets.

Negation is a universal linguistic phenomenon with a great qualitative impact on natural language processing applications. The availability of corpora annotated with negation is essential to training negation processing systems. Currently, most corpora have been annotated for English, but the presence of languages other than English on the Internet, such as Chinese or Spanish, is greater every day. In this study, we present a review of the corpora annotated with negation information in several languages with the goal of evaluating what aspects of negation have been annotated and how compatible the corpora are. We conclude that it is very difficult to merge the existing corpora because we found differences in the annotation schemes used, and most importantly, in the annotation guidelines: the way in which each corpus was tokenized and the negation elements that have been annotated. Differently than for other well established tasks like semantic role labeling or parsing, for negation there is no standard annotation scheme nor guidelines, which hampers progress in its treatment.