NLP
AutoML and Ensemble Transformers for Sentiment Analysis in Mexican Tourism Texts
- AutoML sentiment analysis
- mljar-supervised NLP
- ensemble BERT models
- machine learning Spanish texts
- AI tourism sentiment analysis
- AutoML transformers
- Macro F1 sentiment analysis
- MAE regression NLP
- automated hyperparameter tuning NLP
- IberLEF 2022 challenge
MLJAR tools were used in the following publication.
AutoML and Ensemble Transformers for Sentiment Analysis in Mexican Tourism Texts
Victor Gómez-Espinos, Victor Muñiz-Sanchez, Adrian Pastor López-Monroy
Mathematics Research Center (CIMAT), Monterrey, Mexico; Mathematics Research Center (CIMAT), Guanajuato, Mexico
This study presents a hybrid framework combining ensemble BERT transformers and mljar-supervised AutoML for sentiment analysis in Mexican tourism texts. High-level contextual embeddings were extracted using an ensemble of fine-tuned Spanish BERT models and subsequently optimized using automated machine learning techniques. The AutoML-driven ensemble improved classification and regression performance, achieving Macro F1-score up to 0.9888 for opinion type classification and MAE of 0.2440 for polarity prediction. The proposed methodology obtained third place in the IberLEF 2022 sentiment analysis challenge.
IberLEF 2022 (CEUR Workshop Proceedings) • September 1, 2022
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