文件名称:抑郁症:通过患者健康问卷9和自然语言处理对抑郁症进行分析
文件大小:539KB
文件格式:ZIP
更新时间:2024-03-18 20:21:29
natural-language-processing depression JupyterNotebook
通过患者健康问卷9和自然语言处理对抑郁症进行分析
【文件预览】:
depression-master
----README.md(8KB)
----classifier()
--------0~9+W2V+BiLSTM.ipynb(59KB)
--------Y&N+UNI+NBC.py(1KB)
--------Y&N+W2V+SVM.ipynb(4KB)
--------0~9 classifier.py(4KB)
--------Y&N+UNI+LSTM.py(3KB)
--------0~9+W2V+NBC.ipynb(4KB)
--------Y&N+UNI+BiRNN.py(3KB)
--------0~9+UNI+BiLSTM.py(3KB)
--------0~9+W2V+CNN.ipynb(6KB)
--------Y&N+W2V+LSTM.ipynb(85KB)
--------Y&N+UNI+RNN.py(3KB)
--------Y&N classifier.py(4KB)
--------Y&N classification performance evaluation.py(2KB)
--------0~9+UNI+CNN+ATT.py(4KB)
--------0~9+UNI+BiRNN.py(3KB)
--------Y&N+W2V+BiRNN.ipynb(8KB)
--------Y&N+W2V+CNN.ipynb(6KB)
--------0~9+W2V+BiRNN.ipynb(65KB)
--------0~9+UNI+RNN.py(3KB)
--------0~9 classification performance evaluation.py(2KB)
--------0~9+UNI+LSTM.py(3KB)
--------Y&N+UNI+CNN+ATT.py(4KB)
--------Y&N+W2V+RNN.ipynb(68KB)
--------0~9+UNI+NBC.py(1KB)
--------Y&N+UNI+BiLSTM.py(3KB)
--------0~9+W2V+RNN.ipynb(60KB)
--------Y&N+W2V+BiLSTM.ipynb(6KB)
--------0~9+W2V+LSTM.ipynb(55KB)
--------Y&N+W2V+NBC.ipynb(4KB)
--------Y&N+UNI+SVM.py(1KB)
--------0~9+W2V+SVM.ipynb(4KB)
--------0~9+UNI+SVM.py(2KB)
----data preprocessing()
--------naver blog.py(2KB)
--------naver grammar.py(3KB)
--------naver cafe.py(2KB)
--------descriptive statistics.py(1KB)
----depression score model()
--------descriptive statistics.ipynb(301KB)
--------depression score model.ipynb(135KB)
----data crawling()
--------naver blog.py(4KB)
--------naver cafe.py(2KB)
----.idea()
--------.gitignore(0B)
--------workspace.xml(16KB)
--------vcs.xml(180B)
--------misc.xml(178B)
--------modules.xml(272B)
--------depression.iml(284B)
--------inspectionProfiles()