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https://github.com/stevillis/glassdoor-reviews-report
A report analyzing sentiment predictions made by a BERTimbau-based model on Glassdoor reviews of IT Companies in Cuiabá.
https://github.com/stevillis/glassdoor-reviews-report
bert bertimbau glassdoor ml nlp pytorch sentiment-analysis
Last synced: 4 days ago
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A report analyzing sentiment predictions made by a BERTimbau-based model on Glassdoor reviews of IT Companies in Cuiabá.
- Host: GitHub
- URL: https://github.com/stevillis/glassdoor-reviews-report
- Owner: stevillis
- Created: 2024-04-23T01:56:31.000Z (9 months ago)
- Default Branch: master
- Last Pushed: 2025-01-10T17:23:10.000Z (10 days ago)
- Last Synced: 2025-01-10T18:33:23.913Z (10 days ago)
- Topics: bert, bertimbau, glassdoor, ml, nlp, pytorch, sentiment-analysis
- Language: Jupyter Notebook
- Homepage: https://glassdoor-reviews-report.streamlit.app/
- Size: 3.87 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Glassdoor Reviews Report
This repository contains a sentiment analysis report generated from predicted sentiments made by a model trained to analyze reviews of Cuiabá IT Companies' in Glassdoor. The model harnesses the power of BERT-based architecture, specifically BERTimbau, to predict the sentiment of reviews.
The live preview can be found at: [Análise de sentimento em avaliações no Glassdoor: Um estudo sobre empresas de Tecnologia da Informação em Cuiabá](https://glassdoor-reviews-report.streamlit.app/).
⚠️ If you find the app in **sleep mode**, please hit the button **"Yes, get this app back up!"**.
## Application Overview
### [🏠Home](🏠Home.py)
Serves as the primary access point for the application Loads the necessary data for analysis and implements the following sections:
- **Introduction**: Provides an overview of the report.
- **Positive and Negative Reviews Ranking**: Analyzes and ranks reviews based on sentiment.
- **Company Analysis**: Evaluates company reputation.
- **Sentiment Reviews Over Time**: Tracks sentiment trends across different time periods.
- **Rating Star Analysis**: Examines distribution and patterns of rating stars.
- **Employee Role Analysis**: Investigates reviews based on employee roles.
- **Word Cloud Analysis**: Visual representation of frequently used words in reviews.
- **Most Common Words Analysis**: Lists the most common words in reviews.
- **N-Gram Analysis**: Analyzes most frequent sequences of words.
### [🥇Ranking geral de avaliações](./pages/1_🥇_Ranking%20geral%20de%20avaliações.py)
Shows the number of reviews and the associated sentiment for each company, ordered by the difference between positive and negative reviews.
### [📉 Avaliações ao longo do tempo](./pages/2_📉_Avaliações%20ao%20longo%20do%20tempo.py)
Presents a sentiment analysis of reviews along the time by company.
### [⭐ Avaliações por quantidade de estrelas](./pages/3_⭐_Avaliações%20por%20quantidade%20de%20estrelas.py)
Examines how reviews correlate with star ratings for a chosen company.
### [🧑💼 Avaliações por grupo de funcionários](./pages/4_🧑💼_Avaliações%20por%20grupo%20de%20funcionários.py)
Shows reviews categorized by different employee groups for a specific company.
### [☁️ Word Cloud](./pages/5_☁️_Word%20Cloud.py)
Shows Word Cloud by company.
### [📊 Top 10 palavras mais usadas](./pages/6_📊_Top%2010%20palavras%20mais%20usadas.py)
Shows Top 10 frequent words by company.
### [🔠 NGrams](./pages/7_🔠_NGrams.py)
Shows N-Grams by company.
### [🧠 Treinamento do Modelo.py](./pages/8_🧠_Treinamento%20do%20Modelo.py)
Shows Data Preparation, Model Architecture, Model Training and Model Evaluation.
## How to Run the Application
1. Clone the repository:
```sh
git clone https://github.com/yourusername/glassdoor-reviews-report.git
cd glassdoor-reviews-report
```
2. Install the required dependencies:
```sh
pip install -r requirements.txt
```
3. Run the Streamlit application:
```sh
streamlit run 🏠Home.py
```