Skip to content

Employee Sentiment Track is a project aimed at understanding organizational climate through the analysis of employee opinions. By leveraging sentiment analysis and topic modeling techniques, this project provides valuable insights into the workplace environment.

License

Notifications You must be signed in to change notification settings

Efradgalio/Employee-Sentiment-Tracker

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

57 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Employee-Opinion-Tracker

Python version Latest version GitHub forks GitHub Stars License

Employee Opinion Tracker is a comprehensive aimed at understanding organizational climate through the analysis of employee opinions. By leveraging sentiment analysis and topic modeling techniques, this project provides valuable insights into the workplace environment. The results are presented through an interactive Streamlit dashboard, making it easy to visualize and interpret the data.

To use streamlit_app_openai_chatbot.py you need to insert your OPENAI_API_KEY in ./.streamlit/secrets.toml

Tree Structure

├── .conda
├── .streamlit
├── __pycache_
├── data_preprocessing
│   └── Text Cleaning & Normalization_Ali.ipynb
├── dataset
│   ├── Capgemini_Employee_Reviews_from_AmbitionBox.csv
│   └── data_employee_topic.csv
├── eda
│   └── EDA_Empl_Opinion_Tracker_r2.ipynb
├── sentiment_analysis
│   ├── preprocessing_capegini.xlsx
│   ├── sentiment analysis.ipynb
│   └── sentiment_evaluation_metrics.xlsx
├── style
│   └── style.css
├── topic_modeling
│   ├── Capgemini_Employee_Reviews_Topics.csv
│   ├── final_xgboost_sg_tuned.joblib
│   ├── topic_modeling_capgemini_notebook.ipynb
│   └── word2vec_sg.bin
├── user_employee_feedbacks
│   ├── user_employee_feedbacks.json
│   └── user_feedback.csv
├── .gitignore
├── LICENSE
├── README.md
├── SPARK - Employee Opinion Tracker.pdf
├── bert_sentiment.py
├── data_preprocessing.py
├── inference.py
├── logs.log
├── requirements.txt
├── Result_1.jpeg
├── Result_2.jpeg
├── streamlit_app.py
└── streamlit_app_openai_chatbot.py

Running the App

  • Don't forget to install all the required library

Running the streamlit_app.py:

streamlit run streamlit_app.py

Deployment

Deployment currently in local using Streamlit only. I have included two screenshoots how the Streamlit Dashboard looks like:

result_1 result_2

Detail Presentation

To see the detail Background & Problem statement, Objective & Scope, Workflow, Data Source, Model Evaluation & Performance, and Conclusion please see SPARK - Employee Opinion Tracker.pdf

License

Distributed under the terms of the MIT license, "Employee-Sentiment-Tracker" is free and open source software.

Issues

If you encounter any problems, please file an issue along with a detailed description.

About

Employee Sentiment Track is a project aimed at understanding organizational climate through the analysis of employee opinions. By leveraging sentiment analysis and topic modeling techniques, this project provides valuable insights into the workplace environment.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published