YouTube Text Analysis

About the Project

This analysis aims to explore various aspects of YouTube data, including popular negative and positive comments, an emoji analysis, trending videos, and how punctuation and video category affect likes, views, and comments. Additionally, audience engagement will be studied to identify which types of videos resonate most with viewers. By gaining a deeper understanding of the factors that drive popularity and engagement on YouTube, this analysis aims to provide valuable insights for those looking to create or promote content on the platform.

Summary Project

In this project, data from YouTube comments was imported and analyzed to derive insights about user engagement and content trends. Initial steps involved setting up the environment by importing necessary libraries like pandas, numpy, seaborn, matplotlib, and handling warnings.

Objective

Data Loading and Exploration: Imported and examined the 'UScomments.csv' dataset containing YouTube comments from US-based videos. Conducted preliminary analysis to understand the dataset's structure, including viewing the first few rows and calculating the total number of comments.

Content Analysis:

  • Wordcloud Visualization: Generated a word cloud to visually represent the frequency of words in the comments.

  • Emoji Analysis: Investigated the use and patterns of emojis within the comments to determine their significance and frequency.

  • Category Insights: Analyzed likes across different video categories to identify which categories are most favored by the audience.

  • Audience Engagement: Explored the level of audience engagement in terms of interactions with various videos.

  • Trending Video Patterns: Delved into the factors that contribute to videos becoming 'trending', such as views, likes, and comments.

  • Punctuation Correlation: Assessed whether the use of punctuation in comments or video details correlates with metrics like views, likes, and dislikes.

Visualization

Top 10 trending emojis

Some popular categories

Analyzing whether punctuation affects view, likes, and comments

Categories which audience has much engage

Key Findings

  1. Wordcloud Analysis: Words such as "best," "awesome," "amazing," and "perfect" appeared prominently in the comments, suggesting that these terms or themes are frequently discussed and resonate with the audience.

  2. Emoji Use: Emojis such as "😂", "😍", and "❤" were frequently used in the comments, indicating a trend in expressing emotions or sentiments with these symbols.

  3. Category Engagement: The categories of music, entertainment, and comedy received the most likes, suggesting that they are highly favored by the audience, likely due to their entertainment value.

  4. Audience Interaction: Videos related to entertainment that garnered more than 25,000 views demonstrated higher engagement rates. This suggests that relaxing videos that gain initial traction often maintain sustained engagement.

  5. Trending Video Insights: TV shows typically receive a significant number of likes and comments on the day they are published, playing a pivotal role in a video's 'trending' status.

  6. Punctuation Correlation: Videos that accumulate a high number of views often have a higher count of likes and feature more punctuation in the comments.

Top Positive words

Top Negative words