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PROJECT
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
1
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
2
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
3
Create the API
Task 12: Create a FastAPI
Task 13: Load the Model for Prediction
Congratulations!