SentimentCareBot: Retrieval-Augmented Generation Chatbot for Mental Health Support with Sentiment Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The global mental healthcare system faces various challenges in terms of accessibility and the availability of specialist support, such as psychologists and counselors, especially following the COVID-19 pandemic. This study explores a potential solution to this problem by developing a chatbot model, SentimentCareBot, which integrates sentiment analysis with retrieved-augmented generation (RAG) techniques and Large Language Models (LLMs). The study uses a public Mental Health Counseling Conversations Dataset and baseline selection methods such as Naive RAG, Multi-query RAG, and Hypothetical Document Embeddings (HyDE) to improve query translations. The findings from Tukey's Honest Significant Difference (HSD) test reveals a significant improvement in sentiment analysis performance when it is applied to the Multi-query RAG using the MistralAI language model, compared to both Multi-query RAG using the OpenAI language model and HyDE using OpenAI with Sentiment Analysis. These results demonstrate the potential of sentiment analysis to enhance the effectiveness of mental health chatbots.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it