ENHANCING SOCIAL MEDIA SENTIMENT ANALYSIS AND STRESS DETECTION USING MACHINE LEARNING

Not Approved

Relevance and Originality

The project addresses a crucial and contemporary issue in social media research: enhancing sentiment analysis and stress detection. The integration of advanced machine learning techniques, such as RNNs and transformers, is highly relevant given the data-driven nature of modern social media. The proposed hybrid approach for stress detection, combining lexical and physiological signals, showcases originality and a promising avenue for further research.

Methodology

The description mentions the use of deep learning models and a hybrid approach but lacks specific details on the methodology. Providing information on data collection, preprocessing steps, model architectures, and the rationale for chosen techniques would enhance the clarity and robustness of the methodology. Additionally, outlining how physiological signals are integrated into the analysis would provide deeper insights.

Validity & Reliability

To establish the validity of the findings, the project should discuss the evaluation metrics used for sentiment classification and stress detection, such as accuracy, precision, recall, and F1 score. Including a description of how the models were validated across different demographics and cultural contexts would enhance reliability. Addressing potential biases in the data and methods for mitigating them would further strengthen the study.

Clarity and Structure

While the project overview conveys the key objectives effectively, improving the structure with clearly defined sections—such as introduction, methodology, results, and discussion—would enhance readability. Summarizing the main findings and their implications at the end of each section would facilitate better understanding for readers.

Result Analysis

The project highlights the potential benefits of the proposed framework but lacks specific quantitative results or comparisons with traditional methods. Including performance metrics to demonstrate the effectiveness of the hybrid approach for sentiment analysis and stress detection would enrich the analysis. Additionally, discussing practical implications for applications in e-commerce, healthcare, and public opinion analysis would provide valuable context and relevance.