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