Topic Modeling made easy

Is your organization looking to unlock valuable insights for large amounts of text data for sources like social media posts, product reviews and survey results ? Have you already tried techniques like key words/phrases, sentiment analysis or word cloud, but are looking for deeper insights ? Topic modeling will elevate your Natural Language processing/Text Analytics to the next level and help your organization discover valuable business insights.

This demo filled session uses a real life use case of Team Health Survey data to introduce attendees to the concepts , tools and techniques of topic modeling. Using Jupyter notebook, we will walk through the python code demo for ;

• Data loading, clean-up and pre-processing
• Tokenization, Stop words, n-grams and lemmatization
• Iterate through model training and evaluation
• Visualize output and interpret human readable topics
• Business value and application of topic modeling

Goals

• Understand the concepts and techniques of topic modeling
• Learn how to train and evaluate topic model
• Learn how to visualize its outputs and interpret topics
• Understand the business value and application of topic modeling

Prerequisites

• Basic knowledge of Python
• Basic knowledge of statistics
• Interest in business application & value of Natural Language Processing

Sanil Mhatre is a Lead Data Scientist with background in Business Intelligence, Data Engineering, Database Administration and Data & Cloud Platform Architecture. He leads a team delivering Advanced Analytics for a technology services and solutions company. Sanil has a Master's degree in Information systems and enjoys working with various visualization platforms and coaching high performance teams with Agile best practices. He is an active blogger and a frequent speaker at technical conferences and user groups. He volunteers with STEM mentorship programs and various teaching initiatives. When Sanil isn't working, he enjoys spending time with family & friends, swimming and hiking with his dogs.