Depression: Twitter Dataset + Feature Extraction
This dataset contains 20,000 labelled English tweets of depressed and non-depressed users. The data is collected using the Twitter API and includes feature extraction techniques such as topic modelling and emoji sentiment analysis. It is designed for mental health classification at the tweet level.
Detalyadong Panimula
The Depression: Twitter Dataset + Feature Extraction is a valuable resource for researchers and developers working on mental health classification. It includes 20,000 labelled English tweets, collected using the Twitter API. The dataset provides feature extraction techniques such as topic modelling and emoji sentiment analysis, making it suitable for various machine learning and data analysis projects. The data is essential for understanding and predicting mental health conditions from social media content.
Higit paDataset
Hugging Face Dataset - lsy641/PsyQA
The data is originally source from (Sun et al,2021). (Liu et al, 2023) processed the data to make it a dataset vis huggingface api with taining/validation/testing splitting
ISSP: International Social Science Survey Program
The ISSP is a cross-national collaboration program conducting annual surveys on diverse topics relevant to social sciences. It includes members from various cultures around the globe and provides free access to collected data and documentation.
Hugging Face Dataset - NamelessAI Helply
This paper discusses Helply - a synthesized ML training dataset focused on psychology and therapy, created by Alex Scott and published by NamelessAI. The dataset developed by Alex Scott is a comprehensive collection of synthesized data designed to train LLMs in understanding psychological and therapeutic contexts. This dataset aims to simulate real-world interactions between therapists and patients, enabling ML models to learn from a wide range of scenarios and therapeutic techniques.