Análisis de sentimiento de los mensajes de Twitter respecto a la empresa KFC del primer trimestre en Hispanoamérica 2022
Bibliographic record
Abstract
It was proposed to answer if the machines can really analyzethe sentiments of the tweets, then the messages in Spanish onTwitter that spoke of KFC were analyzed. The tweets werecaptured every day in the time period of the first quarter ofthe year 2022 from the Latin American region, later theywere analyzed by month and for each company mentionedin the tweets, these came to add 39,269 messages for KFC. We focused on discovering what were the feelings related to eachmessage left, for this reason the polarity of the feeling betweenpositive and negative was sought, the first being related to wellbeing,happiness, and love, while the second polarity, negativewas related to discomfort, sadness, and hatred. After obtainingthe polarity, it remained to discover what its degree was, the high,medium and low indicators were used, thus having the degrees:high positives, medium positives, low positives, high negatives,medium negatives, and low negatives. The term neutral or neutralwas used for unpolarized messages, not meaning a feeling, that is,neutral feelings do not exist, it is only the result of the absence ofsufficient data to classify it in some polarity. Everything mentionedwas done through artificial intelligence, but considering that it wassought to answer if the feelings of the text messages can really beanalyzed, that is why two different heuristics were used, MachineLearning and Deep Learning, with them it was possible identifythe polarity and degree of sentiment of Twitter messages regardingthe KFC company in the first quarter in Latin America 2022.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".