/analysis_sentiment

Primary LanguagePython

Sentiment Analysis API

This project is a simple implementation of sentiment analysis using Python. The program trains a machine learning model on a given dataset and provides an API to predict the sentiment of a text.

Folder Structure

  • analysis_sentiment/
  • │
  • ├── app.py
  • ├── requirements.txt
  • ├── domain/
  • │ ├── sentiment_model.py
  • ├── services/
  • │ ├── sentiment_service.py
  • ├── controllers/
  • │ ├── sentiment_controller.py
  • ├── models/
  • │ ├── train_model.py
  • └── data/
  • └── dataset.csv

Installation

  1. Clone this repository:

    git clone https://github.com/username/sentiment_analysis.git
cd sentiment_analysis

  2. python -m venv venv source venv/bin/activate # Linux/MacOS venv\Scripts\activate # Windows

  3. Install dependencies: pip install -r requirements.txt

  4. Training the Model Ensure the dataset is available in the data directory with the name dataset.csv in the following format: Id,Sentiment,Text 1,negative, TOLOL!! Gak ada hubungan nya keguguran dgn pake hijab syar'i yg lo bilang bayi nya kepanasan didalem gak ada hubungan nya woyyyy!! Otak sama jempol lo gak singkron sih ya jadinya asal nulis komentar! 2,negative,Geblek lo tata...cowo bgt dibela2in balikan...hadeww...ntar ditinggal lg nyalahin tuh cowo...padahal kitenya yg oon....

  5. Run the script to train the model: python3 models/train_model.py

Running the Application

Run the Flask application:

Testing the API

  • Use tools like Postman or curl to test the API. Here is an example using curl:
  • curl -X POST http://127.0.0.1:5000/api/predict -H "Content-Type: application/json" -d

The response will be a JSON containing the text sent and the predicted sentiment.

Code Structure

  • app.py: Main file to run the Flask application.
  • domain/sentiment_model.py: Contains the SentimentModel class that loads and uses the model for prediction.
  • services/sentiment_service.py: Contains the SentimentService class that uses SentimentModel for sentiment analysis.
  • controllers/sentiment_controller.py: Contains the route for the Flask API.
  • models/train_model.py: Script to train the model and save it.

Dependencies

  • Flask
  • scikit-learn
  • pandas
  • joblib