The findings from this study can be leveraged to develop more robust and generalizable depression detection systems by:
Model Transferability: Understanding how different lexicons impact model performance across datasets can guide the development of transferable models that can adapt to diverse data sources and modalities.
Feature Engineering: Insights from the study can inform the creation of more sophisticated features that capture nuanced linguistic cues related to depression, improving the model's ability to generalize across varied datasets.
Ensemble Approaches: Combining models trained on both DAIC-WOZ and PRIMATE datasets can lead to ensemble models that leverage the strengths of each dataset, enhancing the system's overall performance and generalizability.
Multi-Modal Integration: Integrating data from multiple modalities, such as text, audio, and video, based on the findings can enable the development of comprehensive depression detection systems that capture a broader range of behavioral and linguistic cues.
Continuous Learning: Implementing continuous learning mechanisms that adapt the model over time based on new data and insights can ensure the system remains effective and up-to-date across different data sources and modalities.