À propos de ce cours : This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.
Créé par : University of Illinois at Urbana-Champaign
Enseigné par : ChengXiang Zhai, Professor
Department of Computer Science
| Informations de base | Cours 3 de 6 dans Data Mining Specialization |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Terminez ce cours pour travailler en vue de :
Spécialisation Data Mining
Ce cours fait partie de la Spécialisation Data Mining de 6 cours proposée par University of Illinois at Urbana-Champaign
Master of Computer Science in Data Science
Ce cours fait partie du programme de 32 credit hours (8 courses) diplômes intégralement en ligne
Programme de cours
SEMAINE 1
Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
2 vidéos, 5 lectures, 1 quiz pour s'exercer
- Reading: Welcome to Text Mining and Analytics!
- Reading: Syllabus
- Reading: About the Discussion Forums
- Reading: Updating your Profile
- Reading: Social Media
- Vidéo: Course Prerequisites & Completion
- Practice Quiz: Pre-Quiz
Noté: Orientation Quiz
Week 1
During this module, you will learn the overall course design, an overview of natural language processing techniques and text representation, which are the foundation for all kinds of text-mining applications, and word association mining with a particular focus on mining one of the two basic forms of word associations (i.e., paradigmatic relations).
9 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Week 1 Overview
- Vidéo: 1.2 Overview Text Mining and Analytics: Part 2
- Vidéo: 1.3 Natural Language Content Analysis: Part 1
- Vidéo: 1.4 Natural Language Content Analysis: Part 2
- Vidéo: 1.5 Text Representation: Part 1
- Vidéo: 1.6 Text Representation: Part 2
- Vidéo: 1.7 Word Association Mining and Analysis
- Vidéo: 1.8 Paradigmatic Relation Discovery Part 1
- Vidéo: 1.9 Paradigmatic Relation Discovery Part 2
- Practice Quiz: Week 1 Practice Quiz
Noté: Week 1 Quiz
SEMAINE 2
Week 2
During this module, you will learn more about word association mining with a particular focus on mining the other basic form of word association (i.e., syntagmatic relations), and start learning topic analysis with a focus on techniques for mining one topic from text.
10 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Week 2 Overview
- Vidéo: 2.2 Syntagmatic Relation Discovery: Conditional Entropy
- Vidéo: 2.3 Syntagmatic Relation Discovery: Mutual Information: Part 1
- Vidéo: 2.4 Syntagmatic Relation Discovery: Mutual Information: Part 2
- Vidéo: 2.5 Topic Mining and Analysis: Motivation and Task Definition
- Vidéo: 2.6 Topic Mining and Analysis: Term as Topic
- Vidéo: 2.7 Topic Mining and Analysis: Probabilistic Topic Models
- Vidéo: 2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 1
- Vidéo: 2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 2
- Vidéo: 2.10 Probabilistic Topic Models: Mining One Topic
- Practice Quiz: Week 2 Practice Quiz
Noté: Week 2 Quiz
SEMAINE 3
Week 3
During this module, you will learn topic analysis in depth, including mixture models and how they work, Expectation-Maximization (EM) algorithm and how it can be used to estimate parameters of a mixture model, the basic topic model, Probabilistic Latent Semantic Analysis (PLSA), and how Latent Dirichlet Allocation (LDA) extends PLSA.
10 vidéos, 2 lectures, 1 quiz pour s'exercer
- Reading: Week 3 Overview
- Vidéo: 3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 1
- Vidéo: 3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 2
- Vidéo: 3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 1
- Vidéo: 3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 2
- Vidéo: 3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 3
- Vidéo: 3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 1
- Vidéo: 3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 2
- Vidéo: 3.9 Latent Dirichlet Allocation (LDA): Part 1
- Vidéo: 3.10 Latent Dirichlet Allocation (LDA): Part 2
- Practice Quiz: Week 3 Practice Quiz
- Reading: Programming Assignments Overview
Noté: Quiz: Week 3 Quiz
Noté: Programming Assignment
SEMAINE 4
Week 4
During this module, you will learn text clustering, including the basic concepts, main clustering techniques, including probabilistic approaches and similarity-based approaches, and how to evaluate text clustering. You will also start learning text categorization, which is related to text clustering, but with pre-defined categories that can be viewed as pre-defining clusters.
9 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Week 4 Overview
- Vidéo: 4.2 Text Clustering: Generative Probabilistic Models Part 1
- Vidéo: 4.3 Text Clustering: Generative Probabilistic Models Part 2
- Vidéo: 4.4 Text Clustering: Generative Probabilistic Models Part 3
- Vidéo: 4.5 Text Clustering: Similarity-based Approaches
- Vidéo: 4.6 Text Clustering: Evaluation
- Vidéo: 4.7 Text Categorization: Motivation
- Vidéo: 4.8 Text Categorization: Methods
- Vidéo: 4.9 Text Categorization: Generative Probabilistic Models
- Practice Quiz: Week 4 Practice Quiz
Noté: Week 4 Quiz
SEMAINE 5
Week 5
During this module, you will continue learning about various methods for text categorization, including multiple methods classified under discriminative classifiers, and you will also learn sentiment analysis and opinion mining, including a detailed introduction to a particular technique for sentiment classification (i.e., ordinal regression).
7 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Week 5 Overview
- Vidéo: 5.2 Text Categorization: Discriminative Classifier Part 2
- Vidéo: 5.3 Text Categorization: Evaluation Part 1
- Vidéo: 5.4 Text Categorization: Evaluation Part 2
- Vidéo: 5.5 Opinion Mining and Sentiment Analysis: Motivation
- Vidéo: 5.6 Opinion Mining and Sentiment Analysis: Sentiment Classification
- Vidéo: 5.7 Opinion Mining and Sentiment Analysis: Ordinal Logistic Regression
- Practice Quiz: Week 5 Practice Quiz
Noté: Week 5 Quiz
SEMAINE 6
Week 6
During this module, you will continue learning about sentiment analysis and opinion mining with a focus on Latent Aspect Rating Analysis (LARA), and you will learn about techniques for joint mining of text and non-text data, including contextual text mining techniques for analyzing topics in text in association with various context information such as time, location, authors, and sources of data. You will also see a summary of the entire course.
8 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Week 6 Overview
- Vidéo: 6.2 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 2
- Vidéo: 6.3 Text-Based Prediction
- Vidéo: 6.4 Contextual Text Mining: Motivation
- Vidéo: 6.5 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis
- Vidéo: 6.6 Contextual Text Mining: Mining Topics with Social Network Context
- Vidéo: 6.7 Contextual Text Mining: Mining Casual Topics with Time Series Supervision
- Vidéo: 6.8 Course Summary
- Practice Quiz: Week 6 Practice Quiz
Noté: Week 6 Quiz
FAQ
Comment cela fonctionne
Coursework
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Créateurs
University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
i have completed the course but didnt receive certificate and there is no option for id verification
Lots of great topics are covered. Would like to see more hands on exercises.
Great course
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IW
In-depth description on the algorithms.
Personally I suggest finish the quiz of the nth week after finishing all the video of (n+1)th week.