Sentiment Analysis of Social Media Data: An End-to-End Machine Learning Project 2

Abstract:

In this article, we will walk you through an end-to-end machine learning project, focusing on sentiment analysis of social media data. We will discuss the project’s file structure, data collection, preprocessing, model training, evaluation, and deployment. Sample code snippets are provided in Python for a better understanding of each step.

Introduction

Sentiment analysis is the process of determining the sentiment or emotion expressed in a piece of text, such as a tweet or a Facebook post. This can be useful for businesses to gauge customer satisfaction, monitor brand reputation, and inform marketing strategies. In this project, we will use machine learning techniques to analyze sentiment in social media data.

Project File Structure The project will be structured as follows:

sentiment_analysis/
│
├── data/
│   ├── raw/
│   ├── processed/
│   └── train_test_split/
│
├── models/
│
├── src/
│   ├── data/
│   │   ├── __init__.py
│   │   ├── collect_data.py
│   │   └── preprocess_data.py
│   │
│   ├── features/
│   │   ├── __init__.py
│   │   └── text_features.py
│   │
│   ├── models/
│   │   ├── __init__.py
│   │   ├── train_model.py
│   │   └── evaluate_model.py
│   │
│   └── utils/
│       ├── __init__.py
│       └── helpers.py
│
├── notebooks/
│
├── requirements.txt
└── README.md

Data Collection