Leveraging BiLSTM-CRF and adversarial training for sentiment analysis in nature-based digital interventions: Enhancing mental well-being through MOOC platforms.

Abstract

Objective: This study aims to leverage annotated textual data from a Massive Open Online Course (MOOC) platform to conduct sentiment analysis of learners' interactions with nature-based digital interventions, which seeks to enhance sentiment classification and provide insights into learners' affective experiences, ultimately facilitating timely psychological interventions and improving curriculum design.

Methods: This study leverages the extensive corpus of annotated textual data available on a MOOC platform, encompassing learners' assessments, inquiries, and recommendations. By performing meticulous sentiment analysis, we aim to understand the subjective sentiments of learners engaging with nature-based digital interventions. To achieve this, we integrate a Bidirectional Long Short-Term Memory (BiLSTM) network with a Conditional Random Field (CRF). The BiLSTM captures word associations in both forward and backward directions, feeding these results into the CRF network to establish the conditional distribution between the feature function and labels. This ensures high-quality feature extraction, precise label assignment, and the derivation of evaluation metrics. Furthermore, adversarial training is introduced to enhance aspect sentiment classification. This involves incorporating perturbations in the embedding space, generating adversarial samples at the embedding layer and semantic feature fusion layer, and combining these with the original samples for model training.

Results: Experimental outcomes demonstrate that the proposed model achieves precision, recall, and F1 scores of 83.71, 85.66, and 84.67 on the SemEval-2014 dataset, and 80.63, 83.06, and 81.76 on the Coursera dataset.

Conclusion: Notably, the sentiment prediction efficacy surpasses that of comparative models, underscoring the proficiency of the proposed scheme. By harnessing the proposed model, educators and administrators can effectively sift through learners' affective information, facilitating timely psychological interventions and curriculum guidance. This study contributes to the growing body of research on digital mental health interventions within natural settings, providing valuable insights into how technology can support and enhance mental well-being in these contexts.