AmaanP314/youtube-comment-sentiment

YouTube Comments Sentiment Analysis Dataset (1M+ Labeled Comments)

Overview

This dataset comprises over one million YouTube comments, each annotated with sentiment labels—Positive, Neutral, or Negative. The comments span a diverse range of topics including programming, news, sports, politics and more, and are enriched with comprehensive metadata to facilitate various NLP and sentiment analysis tasks.

Dataset Contents

Each record in the dataset includes the following fields:

• CommentID: A unique identifier assigned to each YouTube comment. This allows for individual tracking and analysis of comments.
• VideoID: The unique identifier of the YouTube video to which the comment belongs. This links each comment to its corresponding video.
• VideoTitle: The title of the YouTube video where the comment was posted. This provides context about the video's content.
• AuthorName: The display name of the user who posted the comment. This indicates the commenter's identity.
• AuthorChannelID: The unique identifier of the YouTube channel of the comment's author. This allows for tracking comments across different videos from the same author.
• CommentText: The actual text content of the YouTube comment. This is the raw data used for sentiment analysis.
• Sentiment: The sentiment classification of the comment, typically categorized as positive, negative, or neutral. This represents the emotional tone of the comment.
• Likes: The number of likes received by the comment. This indicates the comment's popularity or agreement from other users.
• Replies: The number of replies to the comment. This indicates the level of engagement and discussion generated by the comment.
• PublishedAt: The date and time when the comment was published. This allows for time-based analysis of comment trends.
• CountryCode: The two-letter country code of the user that posted the comment. This can be used to analyze regional sentiment.
• CategoryID: The category ID of the video that the comment was posted on. This allows for analysis of sentiment across video categories.

Key Features:

• Sentiment Analysis: Each comment has been categorized into positive, negative, or neutral sentiment, allowing for direct analysis of emotional tone.
• Video and Author Metadata: The dataset includes information about the videos (title, category, ID) and authors (channel ID, name), enabling contextual analysis.
• Engagement Metrics: Columns such as "Likes" and "Replies" provide insights into comment popularity and discussion levels.
• Temporal and Geographical Data: "PublishedAt" and "CountryCode" columns allow for time-based and regional sentiment analysis.

Data Collection & Labeling Process

• Extraction:
  Comments were gathered using the YouTube Data API, ensuring a rich and diverse collection from multiple channels and regions.
• Sentiment Labeling:
  A combination of advanced AI (using models such as Gemini) and manual validation was used to accurately label each comment.
• Cleaning & Preprocessing:
  Comprehensive cleaning steps were applied—removing extraneous noise like timestamps, code snippets, and special characters—to ensure high-quality, ready-to-use text.
• Augmentation for Balance:
  To address class imbalances (especially for underrepresented negative and neutral sentiments), a comment augmentation process was implemented. This process generated synthetic variations of selected comments, increasing linguistic diversity while preserving the original sentiment, thus ensuring a more balanced dataset.

Benefits for Users

• Scale & Diversity:
  With over 1M comments from various domains, this dataset offers a rich resource for training and evaluating sentiment analysis models.
• Quality & Consistency:
  Rigorous cleaning, preprocessing, and augmentation ensure that the data is both reliable and representative of real-world YouTube interactions.
• Versatility:
  Ideal for researchers, data scientists, and developers looking to build or fine-tune large language models for sentiment analysis, content moderation, and other NLP applications.

Uses:

• Sentiment analysis of YouTube comments.
• Analysis of viewer engagement and discussion patterns.
• Exploration of sentiment trends across different video categories.
• Regional sentiment analysis.
• Building machine learning models for sentiment prediction.
• Analyzing the impact of video content on viewer sentiment.

This dataset is open-sourced to encourage collaboration and innovation. Detailed documentation and the code used for extraction, labeling, and augmentation are available in the accompanying GitHub repository.