Topic: data model use

Confusion: 1361.11

The confusion of a topic is the average number of confusion (play/pause/seek) events per minute across lecture video segments which cover the given topic.

Word Distribution (Ranked by φ)

Relevant Lectures

• 4.9 Contextual Text Mining: Mining Topics with Social Network Context (00:14:43) (7 minutes)
• 2.3 Topic Mining and Analysis: Probabilistic Topic Models (00:14:17) (3 minutes)
• 2.10 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 1 (00:11:05) (3 minutes)
• 4.11 Course Summary (00:18:36) (2 minutes)
• 1.4 Natural Language Content Analysis: Part 2 (00:04:25) (2 minutes)
• 2.6 Probabilistic Topic Models: Mining One Topic (00:12:21) (2 minutes)
• 2.7 Probabilistic Topic Models: Mixture of Unigram Language Models (00:12:39) (2 minutes)
• 2.8 Probabilistic Topic Models: Mixture Model Estimation: Part 1 (00:10:16) (2 minutes)
• 2.11 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 2 (00:10:39) (2 minutes)
• 4.6 Text-Based Prediction (00:12:08) (2 minutes)
• 4.10 Contextual Text Mining: Mining Casual Topics with Time Series Supervision (00:19:37) (2 minutes)
• 1.3 Natural Language Content Analysis: Part 1 (00:12:48) (1 minutes)
• 1.6 Text Representation: Part 2 (00:09:29) (1 minutes)
• 2.4 Probabilistic Topic Models: Overview of Statistical Language Models: Part 1 (00:10:25) (1 minutes)
• 4.4 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 1 (00:15:17) (1 minutes)
• 4.5 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 2 (00:14:43) (1 minutes)
• 4.8 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis (00:17:59) (1 minutes)
• Text Mining and Analytics (00:08:15) (1 minutes)
• 1.1 Overview Text Mining and Analytics: Part 1 (00:11:43) (1 minutes)