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.

Resources:

• Flask Documentation: https://flask.palletsprojects.com/
• Hugging Face Transformers Library: https://huggingface.co/transformers/
• Practical Natural Language Processing by Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana: https://www.amazon.com/Practical-Natural-Language-Processing-Pragmatic/dp/1492054052/

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.