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⚡ 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.

SOTorrent Queries:

On this repository, we mentioned completely all queries that we used for generating the data.

Fast Run (Approach 1):

The 'Results.ipynb' file gathered all information from all library codes and the article plots generated through this file.

Code Structure

├── CSV_data/
├── Excel_data/                     <~~~~ All the sheets related to the empirical studies
├── Pickle_data/
├── Result/                         <~~~~ Result plots
├── ML_ST_hugh_v2.ipynb             =|
├── ML_ST_keras_v2.ipynb             |
├── ML_ST_nltk_v2.ipynb              |
├── ML_ST_pt_v2.ipynb                | <~~~~ All library codes: The structure of all files. 
├── ML_ST_sklearn_v2.ipynb           |       However, there are some small changes in each library. 
├── ML_ST_spark.ipynb                |       (For checking you can check the Spark file.)
├── ML_ST_tf_v2.ipynb               =|
├── README.md
├── Results.ipynb                   <~~~~ You can find all the information that helps us to generate plots and tables.
└── requirements.txt                <~~~~ Requirement packages

Complete Run (Approach 2):

Running the project with the whole dependency files: Downlaod Link

Structure

├── README.md
├── anaconda3.tar.gz                <~~~~ Virtual environment
├── code_output_csv/
├── db_results/                     <~~~~ ML libraries information 
├── project_1_codes                 <~~~~ Source codes
│   ├── CSV_data/
│   ├── Excel_data/
│   ├── Pickle_data/
│   ├── Project_1_hugh_v2.ipynb
│   ├── Project_1_keras_v2.ipynb
│   ├── Project_1_nltk_v2.ipynb
│   ├── Project_1_pt_v2.ipynb
│   ├── Project_1_sklearn_v2.ipynb
│   ├── Project_1_spark.ipynb
│   ├── Project_1_tf_v2.ipynb
│   ├── Result/
│   └── Results.ipynb                <~~~~ Output plots
├── question_tag.csv                 <~~~~ Posts information
├── SOTorrent
│   ├── README.md                    <~~~~ Extract posts from SOTorrent
│   └── sotorrent
  1. Please, change your directory to the place that you downlowded the file.
Linux$ cd [Downloade directory]
  1. Unzip the project file
Linux$ unzip archive-project-1.zip
  1. Change your directory to the root of the project.
Linux$ cd ./archive-project-1
  1. Make a directory for for the "anaconda3" environment and unpack that.
 Linux$ mkdir -p anaconda3
 Linux$ tar -xzf anaconda3.tar.gz -C anaconda3
  1. Active the virtual environment.
 Linux$ source ./anaconda3/bin/activate
  1. Run the project
 (anaconda3) Linux$ jupyter-lab .

⚠️ ** 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