Hope Speech Detection in Social Media English Corpora: Performance of Traditional and Transformer Models
This work addresses the problem of detecting motivational expressions for social media moderation, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled hope speech detection in social media English corpora by evaluating traditional machine learning models and fine-tuned transformers, finding that transformers achieved higher performance with a weighted F1 of 0.79 compared to SVM's macro-F1 of 0.78.
The identification of hope speech has become a promised NLP task, considering the need to detect motivational expressions of agency and goal-directed behaviour on social media platforms. This proposal evaluates traditional machine learning models and fine-tuned transformers for a previously split hope speech dataset as train, development and test set. On development test, a linear-kernel SVM and logistic regression both reached a macro-F1 of 0.78; SVM with RBF kernel reached 0.77, and Naïve Bayes hit 0.75. Transformer models delivered better results, the best model achieved weighted precision of 0.82, weighted recall of 0.80, weighted F1 of 0.79, macro F1 of 0.79, and 0.80 accuracy. These results suggest that while optimally configured traditional machine learning models remain agile, transformer architectures detect some subtle semantics of hope to achieve higher precision and recall in hope speech detection, suggesting that larges transformers and LLMs could perform better in small datasets.