# Negation Detection on Mexican Spanish Tweets:
## 6 Discussion  
As mentioned above, automatic identification of negation is a key task for different Natural Language Processing tasks, for example, sentiment analysis on social media [7]. Our experiments show that a negation identification model trained on reviews (SFU \(\text{Review}_{\text{SP}}\)-NEG corpus) presents lower performance metrics when tested on Twitter messages than when the model is trained specifically on Twitter messages. In Table 8, a drop can be observed in all performance metrics when the model is trained on SFU \(\text{Review}_{\text{SP}}\)-NEG corpus. For example, the recall drop indicates that this model identifies almost 10% fewer negation cues than the model trained on Twitter data.  
In general, the loss of recall on the models trained in a different domain, compared with the performance when using Twitter data is caused by the specific traits existing in the language that is used on social networks. An easy example is the shapes that the simple negation takes on Twitter, going from _no_ to _nel_, _Nelson_, _Nelson Mandela_, _Noooo_, etc.  
The paper deals with two types of language differences, one provided by the platform (reviews vs. Twitter) and the other related to geographical variation (Spain vs. Mexico). We consider that both categories have features that are crucial for defining a text and expected this to be reflected in the results. Experiment 6 shows how training the model on the T-MexNeg obtains better results for this corpus than training on SFU \(\text{Review}_{\text{SP}}\)-NEG. However, Experiment 7 shows that a model trained on the T-MexNeg corpus performed equally to a model trained on the SFU \(\text{Review}_{\text{SP}}\)-NEG, when both are tested on SFU \(\text{Review}SP\)-NEG. These results make it difficult to determine the role of the platform and the geographic affiliation in the performance of the system. More precisely, at this stage of the research, we do not have enough data to establish that the dialectal variant is crucial as it was our intuition. More extensive experiments would likely provide more evidence in this line.  
Given that social media, in any of its shapes, seems to be a type of communication that is going to last and change the human communication interactions for the future, it is necessary to elaborate resources that can account for the phenomena that are specific to Computer-Mediated Communication. This is, in general, a challenge for NLP, and even more for this key but basic issue of negation identification. This challenge involves not only lexical identification, but also dealing with syntactic creativity, and the pragmatics on the web.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Experiment 7** & \\  & **TMN-Trained** & **SFU-Trained** \\ \hline Precision & 0.97 & 0.79 \\ Recall & 0.80 & 0.96 \\ F1 & 0.88 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results for negation cue detection testing on the SFU \(\text{Review}_{\text{SP}}\)-NEG corpus.  
Many of the previous works highlight the importance of negation identification and also use this feature on several tasks for social media [53]. However, most of them tackle the negation identification using rule-based systems due to the lack of annotated corpora [7]. We aim to provide annotated corpora so that the negation identification task in Spanish can be done by machine learning models.