Hands-on Text Mining and Analytics

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Hands-on Text Mining and Analytics

Coursera (CC)
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Opleiderscore: starstarstarstar_halfstar_border 7,2 Coursera (CC) heeft een gemiddelde beoordeling van 7,2 (uit 6 ervaringen)

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Beschrijving

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by:  Yonsei University
  • Taught by:  Min Song, Professor

    Library & Information Technology

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Nog niet gevonden wat je zocht? Bekijk deze onderwerpen: Text Analysis, Data Science, Machine learning, Artificial Intelligence en Engels.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by:  Yonsei University
  • Taught by:  Min Song, Professor

    Library & Information Technology
Level Intermediate Language English, Subtitles: Spanish, Chinese (Simplified) How To Pass Pass all graded assignments to complete the course. User Ratings 4.1 stars Average User Rating 4.1See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Yonsei University Yonsei University was established in 1885 and is the oldest private university in Korea. Yonsei’s main campus is situated minutes away from the economic, political, and cultural centers of Seoul’s metropolitan downtown. Yonsei has 3,500 eminent faculty members who are conducting cutting-edge research across all academic disciplines. There are 18 graduate schools, 22 colleges and 133 subsidiary institutions hosting a selective pool of students from around the world. Yonsei is proud of its history and reputation as a leading institution of higher education and research in Asia.

Syllabus


WEEK 1


Course Logistics and the Text Mining Tool for the Course



4 videos, 1 reading expand


  1. Video: 1.1 Description of the course including the objectives and outcomes
  2. Video: 1.2 Explanations of the y-TextMiner package and the datasets
  3. Video: 1.3 How-to-do: workspace installation and setup
  4. Video: 1.4 How-to-use: the y-TextMiner package (download it at http://informatics.yonsei.ac.kr/yTextMiner/yTextMiner.zip)
  5. Reading: What is Text Mining?
  6. Peer Review: y-TextMiner installation and a simple Java program


WEEK 2


Text Preprocessing



5 videos, 1 reading expand


  1. Video: 2.1 Description of possible project ideas
  2. Video: 2.2 What is text mining?
  3. Video: 2.3 Description of preprocessing techniques
  4. Video: 2.4 How-to-do: normalization including tokenization and lemmatization
  5. Video: 2.5 How-to-do: N-Grams
  6. Reading: Text Preprocessing
  7. Peer Review: Preprocessing Practice


WEEK 3


Text Analysis Techniques



6 videos, 2 readings expand


  1. Video: 3.1 Description of stopword removal, stemming, and POS tagging
  2. Video: 3.2 Explanations of named entity recognition
  3. Video: 3.3 Explanations of dependency parsing
  4. Video: 3.4 How-to-do: stopword removal and stemming
  5. Video: 3.5 How-to-do: NER and POS Tagging
  6. Video: 3.6 How-to-do: constituency and dependency parsing
  7. Reading: Stemming and Lemmatization
  8. Reading: Named Entity Recognition

Graded: Text Analysis Practice

WEEK 4


Term Weighting and Document Classification



5 videos, 2 readings expand


  1. Video: 4.1 Explanations of TF*IDF
  2. Video: 4.2 Explanations of document classification
  3. Video: 4.3 Explanations of sentiment analysis
  4. Video: 4.4 How-to-do: computation of tf*idf weighting
  5. Video: 4.5 How-to-do: classification with Logistic Regression
  6. Reading: Text Classification
  7. Reading: TF-IDF

Graded: Document Classification Practice

WEEK 5


Sentiment Analysis



6 videos, 1 reading expand


  1. Video: 5.1 Explanations of sentiment analysis with supervised learning
  2. Video: 5.2 Explanations of sentiment analysis with unsupervised learning
  3. Video: 5.3 Explanations of sentiment analysis with CoreNLP, LingPipe and SentiWordNet
  4. Video: 5.4 How-to-do: sentiment analysis with CoreNLP
  5. Video: 5.5 How-to-do: sentiment analysis with LingPipe
  6. Video: 5.6 How-to-do: sentiment analysis with SentiWordNet
  7. Reading: Opinion mining and sentiment analysis by Bo Pang and Lillian Lee

Graded: Sentiment Analysis Practice

WEEK 6


Topic Modeling



5 videos, 1 reading expand


  1. Video: 6.1 Description of Topic Modeling
  2. Video: 6.2 Explanations of LDA and DMR
  3. Video: 6.3 Description of Topic Modeling with Mallet
  4. Video: 6.4 How-to-do: LDA
  5. Video: 6.5 How-to-do: DMR
  6. Reading: Introduction to Probabilistic Topic Models by David Blei

Graded: Topic Modeling Practice

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