Creating a Sentiment Prediction API with FastAPI
Social media play a significant role in the lives of millions of people worldwide. Users share their opinions, emotions, and reactions on X (formerly Twitter), creating a valuable source of information. In this article, we will explore how to create a sentiment prediction model using FastAPI, a user-friendly Python framework for web API development.
Prerequisites
Before diving into this project, it’s helpful to have some basic knowledge of Python and machine learning.
Project Structure
For better organization, we will divide the code into multiple files:
main.py: Entry point of the FastAPI application.
model.py: Creation, training, and evaluation of the sentiment prediction model.
preprocessing.py: Data preprocessing code.
api.py: Route management and FastAPI API.
requirements.txt: List of project dependencies.
README.md: Project documentation.
This structure serves as a starting point, but feel free to customize it to suit the specific requirements of your project.
Step 1: Data Collection
For this project, we will use a dataset of pre-labeled tweets with sentiment labels (positive, negative, neutral). You can download this dataset from Kaggle by following this link: Kaggle Dataset — Sentiment140. The dataset has 1600000 rows and 6 columns.
Once you’ve downloaded the data, make sure to place it in your project directory.
Step 2: Data Analysis
Our target attribute is sentiment, and the goal will be to predict the sentiment of a tweet. Let's take a closer look at the data to gain a better understanding of its structure and characteristics. It is important to examine various aspects of the dataset, including descriptive statistics and the distribution of sentiment labels.
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