Enjoy!
1.File: "youtube_trending.py" Description: Fetches the latest trending videos on YouTube and displays their details, such as title, views, likes, and comments.
2.File: "youtube_recommendations.py" Description: Given a YouTube video ID, retrieves a list of recommended videos based on the YouTube recommendation algorithm.
3.File: "youtube_views_analysis.py" Description: Analyzes the view count trends of a YouTube channel over a specific time period, providing insights into the growth or decline of views.
4.File: "youtube_engagement_metrics.py" Description: Calculates engagement metrics, such as likes-to-dislikes ratio and comments-to-views ratio, for a given YouTube video.
5.File: "youtube_comment_sentiment_analysis.py" Description: Performs sentiment analysis on the comments of a YouTube video, providing insights into the overall sentiment of the viewers.
6.File: "youtube_channel_analytics.py" Description: Analyzes various metrics of a YouTube channel, including total views, subscribers, video uploads, and engagement rates.
7.File: "youtube_video_tag_analyzer.py" Description: Retrieves the tags of a YouTube video and performs analysis, such as identifying common tags, tag relevance, and their impact on search results.
8.File: "youtube_comment_wordcloud.py" Description: Extracts comments from a YouTube video and generates a word cloud visualization to showcase the most frequently used words by viewers.
9.File: "youtube_thumbnail_analyzer.py" Description: Analyzes the thumbnails of a YouTube channel's videos, providing insights into common themes, colors, and trends to optimize thumbnail creation.
10.File: "youtube_trending_geolocation.py" Description: Analyzes the geographic distribution of trending videos on YouTube, helping identify regional preferences and trends.
11.File: "YouTubeTranscriptAnalyzer.py" Description: YouTubeTranscriptAnalyzer is a Python-based project designed to analyze YouTube video transcripts for specific keywords. It utilizes the YouTube Data API v3 to fetch the video captions and extract the transcript. By providing a YouTube video ID and a list of keywords, the program counts the occurrences of each keyword in the transcript.
How to Use:
- Obtain a YouTube Data API key by following the instructions provided by Google.
- Set the obtained API key in the api_key variable in the code.
- Specify the YouTube video ID you want to analyze by replacing "YOUR_VIDEO_ID" with the actual video ID.
- Define the keywords you want to search for in the keywords list.
- Run the Python script.
- The program will retrieve the transcript for the specified video and analyze it for keyword counts.
Please note that the code snippets assume the presence of necessary API keys and packages. Make sure to replace "YOUR_API_KEY", "YOUR_VIDEO_ID", "YOUR_CHANNEL_ID" with the appropriate values before running the code.