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Hands-on Text Mining and Analytics

Approx. 13 hours to complete

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Course Summary

Learn how to extract insights from unstructured data sources such as text, social media, and web documents with Text Mining and Analytics course. Explore the basics of text mining and natural language processing techniques, including sentiment analysis and topic modeling. Apply these techniques to real-world problems like product review analysis and social media monitoring.

Key Learning Points

Understand the basics of text mining and natural language processing techniques
Learn how to analyze unstructured data sources such as text, social media, and web documents
Apply text mining techniques to real-world problems like product review analysis and social media monitoring

Job Positions & Salaries of people who have taken this course might have

- USA: $60,000 - $100,000
- India: ₹400,000 - ₹800,000
- Spain: €20,000 - €40,000
- USA: $60,000 - $100,000
- India: ₹400,000 - ₹800,000
- Spain: €20,000 - €40,000
- USA: $50,000 - $90,000
- India: ₹300,000 - ₹700,000
- Spain: €18,000 - €35,000
- USA: $60,000 - $100,000
- India: ₹400,000 - ₹800,000
- Spain: €20,000 - €40,000
- USA: $50,000 - $90,000
- India: ₹300,000 - ₹700,000
- Spain: €18,000 - €35,000
- USA: $50,000 - $90,000
- India: ₹300,000 - ₹700,000
- Spain: €18,000 - €35,000

Learning Outcomes

Understand the basics of text mining and natural language processing techniques
Apply text mining techniques to real-world problems like product review analysis and social media monitoring
Gain skills in sentiment analysis and topic modeling

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming
Familiarity with statistical analysis

Course Difficulty Level

Intermediate

Course Format

Online Self-Paced
Video Lectures
Assignments and Quizzes

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Applied Data Science with Python
Machine Learning

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Description

This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help learners be trained to be a competent data scientists.

Empowered by bringing lecture notes together with lab sessions based on the y-TextMiner toolkit developed for the class, learners will be able to develop interesting text mining applications.

Outline

Course Logistics and the Text Mining Tool for the Course
1.1 Description of the course including the objectives and outcomes
1.2 Explanations of the y-TextMiner package and the datasets
1.3 How-to-do: workspace installation and setup
1.4 How-to-use: the y-TextMiner package (download it at http://informatics.yonsei.ac.kr/yTextMiner/yTextMiner1.2.zip)
What is Text Mining?

Text Preprocessing
2.1 Description of possible project ideas
2.2 What is text mining?
2.3 Description of preprocessing techniques
2.4 How-to-do: normalization including tokenization and lemmatization
2.5 How-to-do: N-Grams
Text Preprocessing

Text Analysis Techniques
3.1 Description of stopword removal, stemming, and POS tagging
3.2 Explanations of named entity recognition
3.3 Explanations of dependency parsing
3.4 How-to-do: stopword removal and stemming
3.5 How-to-do: NER and POS Tagging
3.6 How-to-do: constituency and dependency parsing
Stemming and Lemmatization
Named Entity Recognition

Term Weighting and Document Classification
4.1 Explanations of TF*IDF
4.2 Explanations of document classification
4.3 Explanations of sentiment analysis
4.4 How-to-do: computation of tf*idf weighting
4.5 How-to-do: classification with Logistic Regression
Text Classification
TF-IDF

Sentiment Analysis
5.1 Explanations of sentiment analysis with supervised learning
5.2 Explanations of sentiment analysis with unsupervised learning
5.3 Explanations of sentiment analysis with CoreNLP, LingPipe and SentiWordNet
5.4 How-to-do: sentiment analysis with CoreNLP
5.5 How-to-do: sentiment analysis with LingPipe
5.6 How-to-do: sentiment analysis with SentiWordNet
Opinion mining and sentiment analysis by Bo Pang and Lillian Lee

Topic Modeling
6.1 Description of Topic Modeling
6.2 Explanations of LDA and DMR
6.3 Description of Topic Modeling with Mallet
6.4 How-to-do: LDA
6.5 How-to-do: DMR
Introduction to Probabilistic Topic Models by David Blei

Summary of User Reviews

Learn about text mining and analytics in this highly rated Coursera course. Users praise the course for its practical applications and real-world examples.

Key Aspect Users Liked About This Course

The course provides practical applications and real-world examples.

Pros from User Reviews

Course materials are well-organized and easy to follow
Instructors provide clear explanations and helpful feedback
The course is applicable for both beginners and advanced learners
The exercises and assignments are challenging and engaging
The course offers valuable insights into text mining and analytics techniques

Cons from User Reviews

Some users found the course content too basic
The course can be time-consuming, especially for those with busy schedules
The course may require additional resources for a deeper understanding
Some users experienced technical difficulties with the platform
The course may not be suitable for those without a background in programming or statistics

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