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Hands-on Text Mining and Analytics
  • Level Professional
  • المدة 13 ساعات hours
  • الطبع بواسطة Yonsei University
  • Offered by

عن

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.

الوحدات

Explaining the course and the code base

    • 4 Videos
    • 1 Readings
    • 1 PeerReview
Show info about module content

1 Peer Review

  • y-TextMiner installation and a simple Java program

4 Videos

  • 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)

1 Readings

  • What is Text Mining?

Text preprocessing and social media data collection

    • 5 Videos
    • 1 Readings
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Preprocessing Practice

5 Videos

  • 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

1 Readings

  • Text Preprocessing

Stemming, Lemmatization, POS tagging, Named Entity Extraction, and Dependency Parsing

    • 6 Videos
    • 2 Readings
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Text Analysis Practice

6 Videos

  • 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

2 Readings

  • Stemming and Lemmatization
  • Named Entity Recognition

TF-IDF and Document Classification

    • 5 Videos
    • 2 Readings
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Document Classification Practice

5 Videos

  • 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

2 Readings

  • Text Classification
  • TF-IDF

Sentiment classification with Stanford's RNN, LingPipe's Logistic Regression, and SentiWordNet

    • 6 Videos
    • 1 Readings
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Sentiment Analysis Practice

6 Videos

  • 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

1 Readings

  • Opinion mining and sentiment analysis by Bo Pang and Lillian Lee

Topic Modeling with Mallet's LDA and DMR

    • 5 Videos
    • 1 Readings
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Topic Modeling Practice

5 Videos

  • 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

1 Readings

  • Introduction to Probabilistic Topic Models by David Blei

Auto Summary

Dive into the world of text mining with "Hands-on Text Mining and Analytics." This professional-level course, offered by Coursera, focuses on core text mining techniques using real-world datasets and a Java-based toolkit. Over 780 minutes, learners gain hands-on experience in text preprocessing, sentiment analysis, and topic modeling. Perfect for aspiring data scientists, the course includes lecture notes and lab sessions, with subscription options available for both starter and professional levels.

Min Song
Instructor

Min Song