Building a Sentiment Analysis Web Application with Flask

Sentiment analysis is a natural language processing (NLP) technique used to determine the sentiment (positive, negative, or neutral) of a piece of text. In this tutorial, we’ll create a web application that allows users to analyze the sentiment of text inputs. We’ll use Python, Flask, and a pre-trained machine learning model to perform sentiment analysis.

Tutorial Steps:

  1. Setting Up the Environment:

    • Install Flask using pip: pip install Flask
    • Create a new directory for your project and navigate into it.
  2. Building the Web Interface:

    • Create HTML templates for the user interface using Jinja2 templating.
    • Design a simple form where users can input text for sentiment analysis.
  3. Loading the Pre-trained Model:

    • Choose a pre-trained sentiment analysis model. For example, you can use models available in the transformers library like BERT or DistilBERT.
    • Load the pre-trained model using Hugging Face’s transformers library.
  4. Performing Sentiment Analysis:

    • Define a route in Flask to handle form submissions.
    • Preprocess the user input text (tokenization, padding, etc.) to prepare it for input to the pre-trained model.
    • Use the pre-trained model to predict the sentiment of the input text.
  5. Displaying Results:

    • Render the sentiment analysis results on the web interface.
    • Display the predicted sentiment (positive, negative, or neutral) along with a confidence score.
  6. Improvements and Extensions (Optional):

    • Implement error handling to gracefully handle unexpected inputs or errors during sentiment analysis.
    • Experiment with different pre-trained models or fine-tune models on custom datasets for improved accuracy.
    • Add additional features such as text summarization, entity recognition, or keyword extraction.
  7. Styling with CSS:

    • Enhance the visual appearance of the web application by adding CSS styles.
    • Use CSS frameworks like Bootstrap to create a responsive and aesthetically pleasing interface.
  8. Deployment (Optional):

    • Deploy the Flask application to a web server using platforms like Heroku, PythonAnywhere, or AWS Elastic Beanstalk.
    • Configure environment variables and production settings for security and scalability.


By following this tutorial, you’ll learn how to build a web application for sentiment analysis using Flask and a pre-trained machine learning model. You’ll gain practical experience in web development, natural language processing, and deploying machine learning models in a real-world application.

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