Depression-Detection-on-Reddit
所属分类：物理/力学计算
开发工具：Jupyter Notebook
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上传日期：2024-02-16 11:56:52
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sh-1993
说明： 利用高级自然语言处理（NLP）技术，该项目旨在从Reddit上的文本数据中检测抑郁相关信号。通过结合语言分析工具和机器学习算法，该研究有助于在线社区中心理健康问题的早期识别和支持。
(Utilizing advanced Natural Language Processing (NLP) techniques, this project aims to detect depression-related signals from textual data on Reddit. By combining linguistic analysis tools and machine learning algorithms, the study contributes to early identification and support for mental health issues in online communities.)
文件列表:
Depression Detection in Reddit using NLP.pdf
reddit.ipynb
# Depression-Detection-on-Reddit
## Data Source
The data used in this project is sourced from Identifying-depression (https://github.com/Inusette/Identifying-depression). Credits to the authors and contributors of the original data.
## Introduction
Depression is a widespread mental health condition with debilitating symptoms affecting millions globally. This project explores the use of Natural Language Processing (NLP) methods to identify depressive signals from textual data on platforms like Reddit. The study emphasizes the importance of early detection and the role of linguistic analysis tools.
## Existing Work
Previous research has delved into sentiment analysis and machine learning models to identify mental health concerns in online text. However, challenges in accuracy, specificity, and ethical considerations have been recognized. This project builds upon these foundations, aiming to enhance the understanding of mental health indicators in digital communication.
## Research Gap
Notable research gaps include the need for fine-grained, interpretable models, studies on the effectiveness of mental health interventions on social media, and ethical considerations in automated mental health screening. Closing these gaps is essential for advancing mental health detection on social media responsibly.
## Methodology
Data from diverse online sources underwent meticulous preprocessing, including LIWC analysis and TF-IDF vectorization. MLP and SVM classifiers were employed, with SVM demonstrating superior accuracy (94.8%). The methodology combined linguistic analysis with conventional NLP and machine learning techniques.
## Conclusion and Future Work
Both MLP and SVM classifiers showed strong performance in detecting depression-related content. The study highlights the potential of integrating linguistic analysis with NLP and machine learning for early depression detection. Future work can explore different n-gram ranges, consider recurrent neural networks (RNNs), fine-tune LIWC categories, address class imbalance, and enhance interpretability through techniques like LIME and SHAP.
This project contributes to ongoing efforts in mental health awareness and emphasizes the benefits of leveraging advanced NLP and machine learning techniques for identifying and treating mental health problems in the digital era.
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