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WSQ Text Analytics with R

These are the exercise files used for WSQ Text Analytics with R course.

The course outline can be found in

https://www.tertiarycourses.com.sg/wsq-text-analytics-with-r.html

Topic 1 Overview of Text Mining and Text Analytics

  • Introduction to Text Mining and Text Analytics
  • Applications of Text Mining and Text Analytics for Business Intelligence
  • Cross-Industry Standard Process for Data Mining (CRISP-DM)

Topic 2: Text Cleaning and Pre-processing

  • Install R Text Mining Packages
  • Read In Text Corpus
  • Remove Punctuation and Stop Words
  • Pre-process Text using Tokenization, Stemming, Lemmatization
  • Vectorize Text using Term Frequency (TF) Vectorization, N-gram and Inverse-Document Frequency (TF-IDF)

Topic 3 Text Analytics

  • Part of Speech (POS) Tagging
  • Name Entity Recognition (NER)
  • Text Link Analysis and Feature Engineering

Topic 4: Sentimental Analysis

  • Overview of Machine Learning
  • Install R Machine Learning package
  • Build a Machine Learning Model for Sentimental Analysis
  • Model Evaluation

Topic 5: Text Summarization

  • Summarize Sentiment Analysis
  • Visualize Text Summarization

Mode of Assessment

  • Written Assessment(Q&A)
  • Practical Performance

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Sample Codes for WSQ Text Analytics with R

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