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YouTube Comment Sentiment Analysis

A project for sentiment analysis of YouTube comments using machine learning techniques.

Description

This project aims to perform sentiment analysis on YouTube comments to understand the overall sentiment and opinions expressed by viewers. Sentiment analysis involves classifying text data into positive, negative, or neutral sentiment categories.

The provided Colab notebook demonstrates the following steps for performing sentiment analysis:

  1. Data Collection: The project utilizes the YouTube Data API to fetch comments from specific YouTube videos. The comments are retrieved in text format for further analysis.

  2. Data Preprocessing: The text comments are preprocessed to remove noise, such as special characters, URLs, or emojis. Tokenization and cleaning techniques are applied to convert the comments into a suitable format for analysis.

  3. Sentiment Analysis: The preprocessed comments are fed into a machine learning model to predict the sentiment of each comment. The model can be based on various techniques such as Naive Bayes, Support Vector Machines (SVM), or Recurrent Neural Networks (RNN).

  4. Visualization: The project provides visualizations to present insights from the sentiment analysis, such as sentiment distribution, word clouds, or sentiment trends over time.

Table of Contents

Installation

To run this project, make sure you have the following dependencies installed:

  • Python (version 3.x)
  • Google Colab (for running the provided notebook)

No additional installation steps are required if you are using Google Colab. However, if you want to run the code locally, make sure to install the required libraries specified in the notebook, such as pandas, numpy, nltk, and scikit-learn.

Usage

  1. Open the provided Colab notebook using the following link: YouTube Comment Sentiment Analysis Notebook.

  2. Follow the instructions in the notebook to execute each cell and run the project step by step. Make sure to provide the necessary YouTube API credentials or access tokens as required.

  3. Customize the code as needed to suit your specific analysis requirements or incorporate additional features.

  4. Explore the visualizations and analysis results obtained from the sentiment analysis to gain insights into the sentiment expressed in YouTube comments.

Make sure to customize the README file further to include any additional details or instructions specific to your project.

License

This project is currently not licensed, and all rights are reserved.

Without a license, you do not have permission to use, modify, or distribute the code or any other assets associated with this project.