# Negation Detection on Mexican Spanish Tweets:
## 4 Experiments on Automatic Negation Identification
Basic characteristics of how the word is written are also added to the features, for example, whether the word is constructed by alphanumeric, only alphabet, or only numeric characters. We also identify if the word starts with an uppercase letter, the length of the word, and its part-of-speech tag. We can see the complete features listed below. 1. _WORD.Lower_: The observed word in lowercase. 2. _WORD.Upper_: The observed word in uppercase. 3. _WORD.LastTwoChar_: The last two characters of the observed word. 4. _WORD.LastThreeChar_: The last three characters of the observed word. 5. _WORD.LastFourChar_: The last four characters of the observed word. 6. _WORD.LastFiveChar_: The last five characters of the observed word. 7. _WORD.IsUpper_: Is the observed word is uppercase? 8. _WORD.IsTitle_: Does the observed word begin with a character in uppercase? 9. _WORD.IsDigit_: Is the observed word a numerical character? 10. _POS_: POS Tag of the observed word. 11. _POS.FirstChar_: First two characters of the POS tag. 12. _WORD.IsAlnum_: Is the word constructed by alphanumeric characters? 13. _WORD.IsAlpha_: Is the word constructed only by alphabet characters? 14. _WORD.IsNum_: Is the word constructed only by numeric characters? 15. _WORD.IsLower_: Is the word constructed by lowercase characters? 16. _WORD.Length_: Length of the word. 17. _+1 WORD.Lower_: The lowercase word prior and posterior to the observed word. 18. _+1 WORD.IsTitle_: Does the word prior and posterior to the observed word starts with an uppercase character? 19. _+1 WORD.IsUpper_: Is the word prior and posterior to the observed word uppercase? 20. _+1 POS_: POS Tag of the word prior and posterior 21. _+1 POS_: FirstChar_: First two characters of the POS tag of the word prior and posterior to the observed word. The CRF-based system achieved an F1-score of 0.95 in the experiment of identifying negation cues, improving the results of the dictionary-based system. Various experiments were conducted on both corpora to compare the performance of both systems under different scenarios; for the task of automatic negation cue detection, we only considered simple negation cue class. These experiments were divided as follows:  
* Experiment 1: As a first experiment, the dictionary-based system was applied to detect negation on both T-MexNeg and SFU Review\({}_{\text{SP}}\)-NEG corpora. * Experiment 2: The CRF-based system was trained and tested on the SFU Review\({}_{\text{SP}}\)-NEG corpus for the task of negation cue detection. Additionally, the CRF-based system was trained and tested on the T-MexNeg corpus for the task of automatic negation cue detection. The results of these experiments were then compared to generate reference information between both corpora. * Experiment 3: The CRF-based system was trained and tested on the T-MexNeg corpus to identify only the scope of the negation. Additionally, the CRF-based system was trained and tested on the SFU Review\({}_{\text{SP}}\)-NEG corpus to also identify only the scope of the negation. For evaluating the scope identification, we used the predicted negation cues. * Experiment 4: The CRF-based system was trained and tested on the T-MexNeg corpus to identify only the event of the negation. Additionally, the CRF-based system was trained and tested now on the SFU Review\({}_{\text{SP}}\)-NEG corpus to identify only the event of the negation. For evaluating the event identification, we used the predicted negation cues. * Experiment 5: The CRF-based system was trained and tested on the T-MexNeg corpus to identify negation, scope, and event altogether (Global). Additionally, the CRF-based system was trained and tested on the SFU Review\({}_{\text{SP}}\)-NEG corpus to again identify negation, scope, and event altogether. For evaluating the identification of all tags, we used the predicted negation cues. * Experiment 6: We trained the CRF-based system on the SFU Review\({}_{\text{SP}}\)-NEG corpus and tested on the T-MexNeg corpus for the task of negation detection.