β‘ For the sake of simplicity, the code has been compartmentalized into distinct libraries. Nonetheless, minor adjustments have been made to regular expressions and conditions. If you intend to examine the code, kindly refer to the 'ML_ST_spark.ipynb' file for your perusal.
On this repository, we mentioned completely all queries that we used for generating the data.
The 'Results.ipynb' file gathered all information from all library codes and the article plots generated through this file.
βββ CSV_data/
βββ Excel_data/ <~~~~ All the sheets related to the empirical studies
βββ Pickle_data/
βββ Result/ <~~~~ Result plots
βββ ML_ST_hugh_v2.ipynb =|
βββ ML_ST_keras_v2.ipynb |
βββ ML_ST_nltk_v2.ipynb |
βββ ML_ST_pt_v2.ipynb | <~~~~ All library codes: The structure of all files.
βββ ML_ST_sklearn_v2.ipynb | However, there are some small changes in each library.
βββ ML_ST_spark.ipynb | (For checking you can check the Spark file.)
βββ ML_ST_tf_v2.ipynb =|
βββ README.md
βββ Results.ipynb <~~~~ You can find all the information that helps us to generate plots and tables.
βββ requirements.txt <~~~~ Requirement packages
Running the project with the whole dependency files: Downlaod Link
βββ README.md
βββ anaconda3.tar.gz <~~~~ Virtual environment
βββ code_output_csv/
βββ db_results/ <~~~~ ML libraries information
βββ project_1_codes <~~~~ Source codes
βΒ Β βββ CSV_data/
βΒ Β βββ Excel_data/
βΒ Β βββ Pickle_data/
βΒ Β βββ Project_1_hugh_v2.ipynb
βΒ Β βββ Project_1_keras_v2.ipynb
βΒ Β βββ Project_1_nltk_v2.ipynb
βΒ Β βββ Project_1_pt_v2.ipynb
βΒ Β βββ Project_1_sklearn_v2.ipynb
βΒ Β βββ Project_1_spark.ipynb
βΒ Β βββ Project_1_tf_v2.ipynb
βΒ Β βββ Result/
βΒ Β βββ Results.ipynb <~~~~ Output plots
βββ question_tag.csv <~~~~ Posts information
βββ SOTorrent
βΒ Β βββ README.md <~~~~ Extract posts from SOTorrent
βΒ Β βββ sotorrent
- Please, change your directory to the place that you downlowded the file.
Linux$ cd [Downloade directory]
- Unzip the project file
Linux$ unzip archive-project-1.zip
- Change your directory to the root of the project.
Linux$ cd ./archive-project-1
- Make a directory for for the "anaconda3" environment and unpack that.
Linux$ mkdir -p anaconda3
Linux$ tar -xzf anaconda3.tar.gz -C anaconda3
- Active the virtual environment.
Linux$ source ./anaconda3/bin/activate
- Run the project
(anaconda3) Linux$ jupyter-lab .
β οΈ ** The binary files of the python language are cloned from a system that you can find the information of that in the below box; if you use another processor architecture, you have to install the jupyter-lab on your PC again!
conda version : 4.13.0
conda-build version : 3.21.9
python version : 3.9.12.final.0
virtual packages : __linux=3.10.0=0
__glibc=2.17=0
__unix=0=0
__archspec=1=x86_64
conda av metadata url : None
channel URLs : https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
platform : linux-64
user-agent : conda/4.13.0 requests/2.27.1 CPython/3.9.12 Linux/3.10.0-1160.49.1.el7.x86_64 centos/7.9.2009 glibc/2.17
UID:GID : 1312:1055
netrc file : None
offline mode : False