To further improve the proposed models for higher accuracy and generalizability, especially for more diverse mental health datasets, several strategies can be implemented:
Feature Engineering: Explore more advanced feature engineering techniques such as TF-IDF for word importance, sentiment analysis refinement, and incorporating contextual information like bigrams.
Model Selection: Experiment with a wider range of ML classifiers like Decision Trees, Convolutional Neural Networks (CNN), and BERT-based models to identify the most suitable model for the dataset.
Data Preprocessing: Enhance data cleaning and preprocessing methods by incorporating lemmatization, original NLTK stop word removal, and other NLP techniques to improve data quality.
Balancing Dataset: Address dataset bias and imbalance by implementing rebalancing techniques to ensure a more balanced dataset for training the models.
Ethical Considerations: Consider the ethical implications of using sensitive mental health data and ensure compliance with data privacy regulations to maintain trust and confidentiality.