HRDF Funded Course - Text Mining with R

It is estimated that over 70% of potentially usable 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 analyze 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: Tiny text 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 keywords 
• 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

Dr. Aanand Verma is a Full Stack Data Scientist who once had a torrid love affair with Physics. He has consulted and published in the area of Public Health, Electricity Markets, Telecom, BFSI, Advertising & Communication Strategies and Digital & Social Media Technologies. He has worked on assignments with international agencies such as International Monetary Fund, World Bank, Royal Netherland Embassy etc. besides MNCs like Tata Consultancy Services, Kie Square Consulting and several government organizations of national importance.

He regularly conducts general training programs in Python (Pandas, NumPy, SciPy, Matplotlib, Bokeh), R (dplyr, rstanarm, knitR, ggplot2), Data Visualization (Tableau, D3.js) and Machine Learning (Reinforced Learning, Scikit Learn) and specialized training programs on Structural Equation Modeling and SAP Hana.

He holds a doctorate in Operations Research from Indian Institute of Management Ahmedabad and a post-graduate in Physics from the University of Mumbai. He has advanced training in mathematical programming including optimization, advanced multivariate data analysis, and simulation techniques. When he is not teaching or consulting he can be found meditating or heading for an adventurous trek.