Create and Deploy an API for Sentiment Analysis Using TF-IDF

In this project, we’ll learn to train the machine learning models using the TF-IDF of the textual dataset. After building the model, we’ll use the trained models to create a real-time API using FastAPI.

You will learn to:

Load and preprocess the Amazon review sentiment dataset.

Preprocess and convert the text dataset into TF-IDF.

Implement a multinomial logistic regression classifier from the scratch for sentiment analysis.

Evaluate the model and display performance metrics using scikit-learn and Matplotlib libraries.

Save and load the trained model.

Use the trained model to create a real-time API.

Skills

Natural Language Processing

Machine Learning

Prerequisites

Intermediate knowledge of Python

Familiarity with machine learning models

Basic understanding of NLP concepts

Basic understanding of supervised learning

Technologies

Python

Matplotlib

Scikit-learn

Project Description

This project will use a systematic methodology to conduct sentiment analysis on Amazon product reviews. First, we’ll explore and understand the dataset’s properties, such as its size, the content of its reviews, and the labels corresponding to each review’s positive, negative, or neutral sentiment.

After data preparation, we’ll undertake exploratory data analysis to learn more about how sentiment labels are distributed within the dataset. This will offer insightful advice on how to strike a balance between favorable, unfavorable, and neutral thoughts in Amazon reviews. Once ready, the data will be converted into Term Frequency-Inverse Document Frequency (TF-IDF) representations. A numerical statistic called the TF-IDF measures the significance of words in documents about a group of documents. Thanks to this change, we can use numerical data for sentiment analysis.

We’ll also examine the confusion matrix to understand how well the model can categorize reviews according to various types of emotion. We’ll develop a web-based FastAPI so users can access sentiment analysis.

Project Tasks

Data Preprocessing

Task 0: Get Started

Task 1: Import Modules

Task 2: Load and Describe the Dataset

Task 3: Plot the Distribution of Polarity

Task 4: Plot the Sentiment Distribution

Task 5: Transform the Data to TF-IDF

Task 6: Save the Vectorizer

Build, Train, and Validate the Model

Task 7: Split the Dataset for Training and Testing

Task 8: Build the Model

Task 9: Train the Model

Task 10: Evaluate the Model

Task 11: Save the Model

Create the API

Task 12: Create a FastAPI

Task 13: Load the Model for Prediction

Congratulations!

Relevant Courses

Use the following content to review prerequisites or explore specific concepts in detail.