# Negation Detection on Mexican Spanish Tweets:
###### Abstract  
In this paper, we introduce the T-MexNeg corpus of Tweets written in Mexican Spanish. It consists of 13,704 Tweets, of which 4895 contain negation structures. We performed an analysis of negation statements embedded in the language employed on social media. This research paper aims to present the annotation guidelines along with a novel resource targeted at the negation detection task. The corpus was manually annotated with labels of negation cue, scope, and, event. We report the analysis of the inter-annotator agreement for all the components of the negation structure. This resource is freely available. Furthermore, we performed various experiments to automatically identify negation using the T-MexNeg corpus and the SFU ReviewSP-NEG for training a machine learning algorithm. By comparing two different methodologies, one based on a dictionary and the other based on the Conditional Random Fields algorithm, we found that the results of negation identification on Twitter are lower when the model is trained on the SFU ReviewSP-NEG Corpus. Therefore, this paper shows the importance of having resources built specifically to deal with social media language.  
negation; machine learning; Mexican Spanish; Twitter +
Footnote †: journal: Medical  
2020 [6] and 9.5 million users on Twitter ([[https://es.statista.com/estadisticas/1172236/numero-de-usuarios-activos-mensuales-twitter-mexico-sistema-operative](https://es.statista.com/estadisticas/1172236/numero-de-usuarios-activos-mensuales-twitter-mexico-sistema-operative)]([https://es.statista.com/estadisticas/1172236/numero-de-usuarios-activos-mensuales-twitter-mexico-sistema-operative](https://es.statista.com/estadisticas/1172236/numero-de-usuarios-activos-mensuales-twitter-mexico-sistema-operative)); accessed on 12 February 2021). This causes dialect diversification, not only in the lexicon but also in morphology, syntax, and several slang expressions. Therefore, the language on the Internet [2], WhatsApp, Twitter, Facebook, etc., also shows great diversity. Because of this, it is crucial to have a corpus of reference of negation in Mexican Spanish based on social media language.  
However, to the best of our knowledge, there are no Spanish corpora that are annotated with negation in Twitter in Spanish [7]. This paper presents T-MexNeg, the first corpus annotated with negation in Twitter in Mexican Spanish. Although the main objective of this paper is not to study the language of the Internet, this work can be a solid contribution to the topic, providing data for future studies.  
The present work is structured as follows. Section 2 is devoted to explaining some related literature involving corpora of negation in English and Spanish. Section 3 introduces our corpus of Mexican Spanish Tweets and the annotation protocol, explaining the main criteria for annotation, and the specific tags we adopted. The obtained resource is freely available and can be used in different natural language processing pipelines ([[https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish](https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish)]([https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish](https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish)); accessed on 12 February 2021). We explain the design of the experiments to automatically identify negation in Section 4 and the results of the annotation and the experiments in Section 5. Section 6 discusses the results and the challenges that we faced during annotation. The paper concludes with final remarks and future work in Section 7.