YouTube Data Analysis Project This project analyzes YouTube data to uncover meaningful insights such as sentiment analysis, emoji usage, word cloud visualization, and trends in audience engagement. It demonstrates the application of Python for data cleaning, analysis, and visualization, showcasing the data analyst's skill set.
Objectives The main objectives of this project are:
Data Cleaning and Preprocessing: Ensure data quality by handling duplicates, null values, and inconsistencies. Sentiment Analysis: Determine the overall sentiment (positive, neutral, or negative) of user comments. Emoji and Word Cloud Analysis: Extract and visualize the most frequently used emojis and words in comments. Engagement Trends: Analyze trends in likes, comments, and shares over time to derive audience engagement insights. Trending Categories: Identify categories with the highest engagement and trends. Key Insights Positive sentiments dominate YouTube user comments. Emojis related to joy and humor are the most frequently used. Categories such as Music and Gaming attract the highest audience engagement. Audience engagement, measured by likes and comments, peaks during certain timeframes. Steps Followed
- Data Preprocessing Handled missing and duplicate entries. Standardized text data for NLP (natural language processing) tasks. Cleaned and tokenized comments for analysis.
- Analyses Conducted Sentiment Analysis: Used TextBlob to classify comments. Emoji Analysis: Explored the frequency and type of emojis in user interactions. Trending Insights: Identified categories, time periods, and videos with the highest engagement. Word Cloud: Visualized the most commonly used words in comments.
- Visualization A range of visualizations were created to illustrate findings:
Sentiment Analysis: Pie chart showing sentiment distribution. Word Cloud: Highlighting frequent words in user comments. Emoji Analysis: Bar chart of top-used emojis. Engagement Trends: Line chart for likes, comments, and shares over time. Category Trends: Bar chart for top-performing categories. Tools & Technologies This project uses the following tools and libraries:
Data Manipulation: Pandas, NumPy Data Visualization: Matplotlib, Seaborn, WordCloud Text Analysis: TextBlob, re (regular expressions) Interactive Environment: Jupyter Notebook