https://github.com/machphy/fb_sentiment_analysis

https://github.com/machphy/fb_sentiment_analysis

Last synced: 2 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/machphy/fb_sentiment_analysis
• Owner: machphy
• Created: 2024-07-09T04:29:43.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-09T04:54:51.000Z (4 months ago)
• Last Synced: 2024-07-09T07:21:33.454Z (4 months ago)
• Language: Python
• Size: 1.35 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
![image](https://github.com/machphy/fb_sentiment_analysis/assets/115711628/7673e492-a106-44eb-b158-27584eb53140)# Facebook Sentiment Analysis

This project performs sentiment analysis on Facebook posts using Python. The analysis pipeline involves loading data from a CSV file, preprocessing the data, performing sentiment analysis, and visualizing the results.

## Project Structure

fbsa/

│

├── data/

│ └── fb_scrapped_data.csv # Your CSV dataset file

│

├── scripts/

│ ├── load_data.py # Loads the dataset from CSV

│ ├── preprocess_data.py # Preprocesses the dataset

│ ├── sentiment_analysis.py # Performs sentiment analysis

│ └── visualize_results.py # Visualizes the results

│

├── main.py # Main script to run the analysis pipeline

└── README.md # Project README file

## Project Details

This project is designed to analyze the sentiment of Facebook posts. Sentiment analysis is a natural language processing (NLP) technique used to determine the emotional tone behind a body of text. It is commonly used to understand opinions, attitudes, and emotions expressed in online reviews, social media, and other forms of text.

### Key Features

- **Data Loading**: Loads Facebook post data from a CSV file.

- **Data Preprocessing**: Cleans and preprocesses the data to make it suitable for analysis.

- **Sentiment Analysis**: Analyzes the sentiment of each Facebook post using a sentiment analysis model.

- **Visualization**: Visualizes the distribution of sentiment scores using a histogram.

### Technologies Used

- **Python**: The programming language used for the entire project.

- **Pandas**: A data manipulation and analysis library used to handle the dataset.

- **Matplotlib**: A plotting library used to visualize the results.

Scripts Overview

main.py

The main entry point of the project. It orchestrates data loading, preprocessing, sentiment analysis, and result visualization.

load_data.py

Defines a function to load the dataset from a CSV file.

preprocess_data.py

Defines functions for preprocessing the dataset.

sentiment_analysis.py

Defines functions for performing sentiment analysis.

visualize_results.py

Defines functions for visualizing the results.

### result 

1. **Resulting Graph Image**

   Sentiment analysis sample: