https://github.com/machphy/senti-analysis-flask

https://github.com/machphy/senti-analysis-flask

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• Host: GitHub
• URL: https://github.com/machphy/senti-analysis-flask
• Owner: machphy
• Created: 2024-07-02T15:46:16.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-08T04:35:51.000Z (4 months ago)
• Last Synced: 2024-07-08T05:44:21.840Z (4 months ago)
• Language: Python
• Size: 1.07 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# senti-analysis-flask

structure 

sentiment_analysis_project/

│

├── app.py

├── data_preprocessing_and_model_training.py

├── sentiment_model.pkl

└── templates/

    ├── index.html

    └── result.html

Sentiment Analysis Flask Application

This project implements a sentiment analysis web application using Flask. It allows users to input text and get a prediction of whether the sentiment is positive or negative based on a pre-trained model.

Setup

Prerequisites

Python 3.x installed on your system

Git installed on your system (optional, for cloning the repository)

Installation

Clone the repository:

bash

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git clone https://github.com/machphy/senti-analysis-flask.git

cd senti-analysis-flask

Install dependencies:

bash

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pip install -r requirements.txt

Data Preprocessing and Model Training

Before running the application, the sentiment analysis model (sentiment_model.pkl) needs to be trained. Follow these steps:

bash

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python data_preprocessing_and_model_training.py

This script downloads the movie reviews dataset, preprocesses the data, trains a TF-IDF vectorizer and a Multinomial Naive Bayes classifier, evaluates the model's accuracy, and saves it as sentiment_model.pkl.

Running the Application

To start the Flask application locally:

bash

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python app.py

The application will run on http://127.0.0.1:5000/ (or http://localhost:5000/). Open this URL in your web browser to access the application.

Usage

Enter text into the provided text area on the homepage.

Click the "Analyze" button to submit the text for sentiment analysis.

View the predicted sentiment result on the result page.

Files and Directories

app.py: Flask application script handling web routes and model integration.

data_preprocessing_and_model_training.py: Script for data preprocessing, model training, and saving the trained model.

sentiment_model.pkl: Pre-trained sentiment analysis model.

templates/: Directory containing HTML templates (index.html, result.html) for the web interface.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT