HRDF Funded Course Text Mining with R

It is estimated that over 70% of potentially useable business information is unstructured, often in the form of text data. Text mining provides a collection of techniques that allow us to derive actionable insights from these data.

This course will show you the various tools and major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches, to making sense of unstructured data. Work with a live example of extraction of data from Web and perform all the facets of text mining using R.

The topics include:

• Sentiment analysis
• Word cloud
• Ngrams
• Topics Modeling
• LDA
• Extracting text from social media

Module 1: Introduction

• What is text mining
• Applications of text mining

Module 2: Basic Text Functions

• Text manipulation functions
• Working with strings
• Working with gsub
• Advanced methods
• Convert to corpus

Module 3: Importing Data

• Converting docx into corpus
• Converting pdf into corpus
• Converting html to corpus
• Web scraping

Module 4: Tidytext Package

• Tidying text objects
• Tidying document term matrix objects
• Tidying document frequency matrix objects
• Tidying corpus objects
• Mining literacy works

Module 5: Word Frequencies & Relationships

• Pre-processing text
• Wordcloud
• Frequency analysis
• nGrams & bigrams
• Bigrams for sentiment analysis
• Visualizing bigrams network

Module 6: Sentiment Analysis

• Sentiment libraries
• Analyzing positive & negative words
• Comparing 3 sentiment libraries
• Common positive & negative words

Module 7: Topic Modelling

• Latent Semantic Indexing (LSI)
• Latent Dirichlet Allocation (LDA)
• Word topic probabilities
• Document - topic probabilities
• Chapters probabilities
• Per document classification

Module 8: Document Similarity & Classifier

• Text alignment & pairwise comparison
• Minihashing and locality sensitive hashing
• Extract key words 
• Classify by location, language, topic

Module 9: Working internet and social media (Optional)

• Extracting data from amazon
• Extracting data from twitter
• Extracting youtube comments
• Extracting facebook comments