TigerGraph's Video-Recommendation-System 🐯

Summary

This video showcases the development of an advanced video recommendation system powered by TigerGraph Cloud and Plotly Dash. Users can search for videos based on keywords, view timestamps for keyword mentions, and explore dynamic visualizations such as sentiment analysis and topic modeling. The system utilizes advanced techniques like Latent Dirichlet Allocation (LDA) for topic extraction, network graphs for video relationships, and sentiment analysis for video tone. With interactive features and real-time recommendations, this system offers an immersive way to discover and analyze videos, creating a rich and engaging user experience.

Takeaways

• 😀 This project demonstrates a video recommendation system built using TigerGraph, Plotly Dash, and Plotly, which analyzes videos based on keywords and visualizes relevant data.
• 🎥 Users can input keywords to find videos containing those keywords, along with timestamps where the keywords appear, providing a dynamic video search experience.
• 💡 The system provides video recommendations based on keyword relevance and calculates sentiment scores from video transcripts to better understand video content.
• 📊 The dashboard offers various visualizations, including video analytics, topic modeling, entity recognition, and sentiment analysis, to enrich the user's understanding of video content.
• 🔍 The main page allows users to select keywords, view corresponding videos, and explore timestamps for video content where those keywords appear.
• 🧠 Topic modeling using techniques like LDA (Latent Dirichlet Allocation) is applied to video transcripts to identify dominant topics, assisting in content analysis and visualization.
• 🌐 Multi-page functionality is used to manage interactions across different views, where the URL changes to reflect the current page state, and selections are passed between pages seamlessly.
• ⚙️ The video recommendation system uses TigerGraph queries, particularly a 'get recommendation' query, to find videos with similar content based on Jaccard similarity scores of transcripts.
• 📊 Dash Cytoscape is employed to visualize relationships between video themes, topics, keywords, and video contents, providing an interactive graph experience.
• 😊 Sentiment analysis, using tools like VADER, helps assess the emotions of the video transcripts, categorizing content as positive or negative to further inform recommendations.
• 📈 Entity recognition and TF-IDF (Term Frequency-Inverse Document Frequency) keyword extraction are also utilized to identify key entities and significant terms from video transcripts for better summarization and search functionality.

Q & A

What is the main purpose of the video recommendation system described in the transcript?

The main purpose of the video recommendation system is to help users find and explore videos based on specific keywords. It provides recommendations by calculating Jaccard similarity scores between videos, displays timestamps of keyword occurrences, and offers detailed analytics on video content, such as sentiment analysis and topic modeling.

What are the primary features of the video recommendation system?

The primary features include keyword-based video search, timestamp pinpointing of keyword occurrences, video recommendations using Jaccard similarity, video analytics (such as sentiment analysis and entity recognition), and graph-based visualizations of the relationships between keywords, themes, and videos.

How does the Jaccard similarity function in the recommendation system?

Jaccard similarity is used to compare the similarity between videos based on shared keywords. By calculating the ratio of the intersection of keywords to the union of keywords across videos, the system identifies the most similar videos to a given selection.

What role does Plotly Dash play in the video recommendation system?

Plotly Dash is used to create the user interface for the video recommendation system. It provides interactive components such as dropdowns, buttons, and dynamic pages to navigate through videos, view recommendations, and explore detailed analytics like sentiment scores and topic modeling.

What is the purpose of the topic modeling feature in the system?

Topic modeling, using Latent Dirichlet Allocation (LDA), is used to identify the underlying topics within video transcripts. It helps categorize videos by themes and enables users to explore the relationships between different keywords and topics in the dataset.

How is sentiment analysis integrated into the system?

Sentiment analysis is integrated using the Vader sentiment analyzer, which analyzes the emotional tone of video transcripts. The sentiment scores (positive, negative, or neutral) are displayed to help users understand the emotional context of the video content.

What kind of visualizations are provided in the video recommendation system?

The system uses Dash Cytoscape to visualize the relationships between videos, keywords, and themes. It also provides other visual analytics, including sentiment analysis graphs and topic modeling visualizations, to give users a deeper understanding of the content.

How are entity recognition and topic modeling utilized in the system?

Entity recognition classifies named entities such as people, organizations, and dates in video transcripts. Topic modeling, on the other hand, categorizes videos into distinct themes, allowing users to see the connections between topics, keywords, and videos.

How does the multi-page navigation in the user interface work?

The multi-page navigation allows users to switch between different pages in the system, such as the main video recommendation page, the explore visuals page for analytics, and the about page. Callback functions ensure smooth transitions, dynamically updating the interface based on user actions.

What are the system’s setup requirements and how are queries executed?

The system is set up on TigerGraph Cloud, where users upload data, install pre-built solutions, and run queries through the platform. The main query executed is the *get recommendation* query, which uses Jaccard similarity to find related videos, and other queries to gather sentiment and entity data.