Comments Analysis Dashboard

Analyzed by- Saddam Ansari -Data & Reporting Analyst

About the Project

This project involves analyzing the comment dataset for a YouTube video featuring a collaboration song by Chris Brown and Davido. The primary objective is to derive insightful information from the comments to understand viewer engagement, sentiment, and key discussion points related to the video.

About the Dataset

🚀Source: The dataset is extracted from the comment section of a YouTube video.

✨Size: The dataset contains several thousand rows, each representing a comment along with related metadata.

✨Columns: The dataset includes the following columns:

• comment_id: Unique identifier for each comment.
• comment_text: Text content of the comment.
• author: Username of the comment author.
• likes: Number of likes the comment received.
• replies: Number of replies to the comment.
• timestamp: Timestamp when the comment was posted.

Project Objective

The main goal of this project is to analyze the YouTube comments to extract meaningful insights that can help understand:

• The overall engagement on the video (total comments, likes, replies).
• Popularity of artists mentioned in the comments.
• References to different music styles in the comments.
• Patterns in comment activity over different time periods.
• Identification of the most active commenters.
• Trends in comments over time to see how engagement evolves.

Tools Used

• Power BI: For data visualization and creating interactive dashboards.
• Excel: For initial data cleaning and preparation.

Detailed Explanation of the Dashboard Components

1. Total Comments, Likes, and Replies

• Total Comments: Displays the total number of comments received on the video.
• Total Likes on Comments: Shows the aggregate likes received on all comments.
• Total Replies to Comments: Indicates the number of replies made to the comments.
• Percentage of Likes on Comments: Represents the ratio of likes to comments, providing a measure of engagement quality.

2. Popularity of Artists Mentioned in Comments

This section analyzes how often Chris Brown and Davido are mentioned in the comments compared to other unspecified mentions. It provides insights into which artist is more frequently discussed by the viewers.

3. Music Style References in Comments

This chart identifies the mention of different music styles (like Amapiano, Afrobeat) within the comments. It helps to understand the viewers' perception and discussion about the music genre of the song.

4. Viewer with Multiple Comments

Lists the usernames of viewers who have made multiple comments, highlighting the most engaged users in terms of comment frequency.

5. Comments Activity by Time of Day

Analyzes the volume of comments posted at different times of the day. This helps in understanding the peak times for viewer engagement.

6. Total Comments by Day

Displays the total number of comments received each day, helping to identify specific days with the highest engagement.

7. Total Comments Trends

Shows the trend of comments over a period, indicating how engagement changes over time. This is useful for understanding the long-term interaction pattern of the viewers with the video.

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Insights and Learnings from the Dashboard

• Engagement Peaks: The highest engagement occurs on Mondays with significant peaks on specific days. This could indicate when viewers are most active.

• Top Commenters: Identifying the top commenters helps in recognizing the most loyal and engaged audience members.

• Artist Mentions: Chris Brown and Davido are both frequently mentioned, but there is a substantial number of unspecified mentions, indicating general discussion about the song or other topics.

• Music Style Discussions: There are no specific mentions of music styles like Amapiano or Afrobeat, suggesting that viewers might be more focused on the artists or other aspects of the song.

• Time of Day Activity: The late afternoon (3:00 PM - 5:59 PM) sees the highest comment activity, which can be useful for timing future video releases or promotions.

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Learning Outcomes

From this project, I have learned:

• 1. Advanced Data Visualization: Enhanced my skills in using Power BI to create visually appealing and informative dashboards.

• 2. Data Cleaning and Preparation: Improved my ability to clean and prepare large datasets for analysis.

• 3. Insight Extraction: Developed a better understanding of how to extract and present insights from user-generated content.

• 4. User Engagement Analysis: Gained knowledge in analyzing user engagement patterns over time and across different metrics.

• 5. Effective Communication: Learned to communicate complex data insights in a simple and accessible manner through dashboards.

This project showcases the capability to turn raw data into actionable insights that can drive strategic decisions for content creators and marketers. It highlights the importance of understanding audience engagement to improve future content and interaction strategies.

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How you can help me:

I've successfully completed over 80 Power BI projects, all showcased in my Novypro portfolio. You're all invited to visit my portfolio and explore these amazing projects!

Additionally, I'm currently seeking internship or entry-level opportunities. If you have any opportunities available or need a freelance Power BI project completed, please connect with me on LinkedIn.

Looking forward to connecting with you all!

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Created and Presented by-

Saddam Ansari @Aspiring Data Analyst LinkedIn Location: India THE END