# Negation Detection on Mexican Spanish Tweets:
## 7 Conclusions and Future Work  
This paper presents the first corpus of Twitter messages in Mexican Spanish with negation annotations. The work explains the process of compilation and tagging of the corpus, as well as the elaboration of the annotation protocol. Additionally, we present experiments on automatic detection of negation terms, events, and scope. We trained a classification algorithm in the TNM corpus and evaluated the model using cross-validation. The obtained results show that the identification of negation terms, event, and scope on Twitter is a challenging task, and it is even harder if the models are trained on corpora of a different domain.  
The texts were collected in the geographical area of Mexico, which means that most of them belong to the Mexican variant of Spanish, which is the Spanish dialect with the most speakers in the world [6]. Moreover, they belong to a micro-blogging platform, Twitter. Therefore, we faced the challenge to adapt the annotation to the Mexican variant and the specificity of netspeak.  
First, there are different variants in the spellings of words--_unuka_, _nunk_, _nunca_, _nuncA_, _nuncaaaaaa_, etc. Then, there are the many typographies for capturing pragmatic traits: emojis, capital letters, etc. Moreover, the texts present features such as abbreviations and contractions--_tombre_ instead of _no hombre_--and the use of alternative words--_tel_, _nelson_, or _nop_ instead of _no_. Other problems were external references to other tweets that were not in the corpus and Internet slang.  
This work deals with syntactic negation, the one built with words that express that something is not occurring through the syntactic structure. In the future, similar resources can be created, covering also lexical negation, the one produced through a morphological change in a word. As an example, _I do not come_ can be expressed as _It is impossible for me to come_.  
Additionally, the results show how necessary it is to elaborate resources that are explicitly oriented to detect negation in different variants of Spanish.  
Finally, another future line of work is building negation corpora based on other social media platforms, such as Facebook, Instagram, or WhatsApp, to better capture the dynamics and idiosyncratic expressions of negation on the web. On this same line, we plan to use previously published corpora that are already accepted by the research community such as those published in TASS@SEPLN ([[http://tass.sepln.org/tass_data/download.php?auth=QtDa3s5](http://tass.sepln.org/tass_data/download.php?auth=QtDa3s5) sA4 ReWvYeWrf]([http://tass.sepln.org/tass_data/download.php?auth=QtDa3s5](http://tass.sepln.org/tass_data/download.php?auth=QtDa3s5) sA4 ReWvYeWrf); accessed on 15 February 2021 ).  
Conceptualization, G.B.-E. and H.G.-A.; Data curation, S.-L.O.-T. and B.A.-V.; Formal analysis, A.P.; Methodology, G.B.-E., H.G.-A., A.P., S.-L.O.-T. and B.A.-V.; Resources, A.P.; Software, B.A.-V.; Supervision, G.B.-E. and H.G.-A.; Validation, G.B.-E. and H.G.-A.; Visualization, B.A.-V.; Writing--original draft, S.-L.O.-T. ; Writing--review & editing, G.B.-E., H.G.-A. and B.A.-V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CONACyT project CB A1-S-27780, DGAPA-UNAM PAPIIT grants number TA400121 and TA100520.  
Publicky available datasets were analyzed in this study. This data can be found here: [[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 15 February 2021.  
The authors declare no conflict of interest.