Abstract: 

Natural language processing has exploded in popularity during the last decade. No longer confined to academia, many companies now see NLP as a critical portion of their business intelligence, with the NLP market size expected to double again in the next two years. Traditional NLP approaches like sentiment analysis and topic modeling provide undeniably meaningful insights, but what other techniques can be leveraged to mine information from text?

This talk focuses on lesser known NLP methods that can help unearth novel observations and make analyses more memorable. After a brief introduction to the topic, attendees will learn about various open-source Python packages they can apply to enhance their NLP workflows. Example use cases will also be discussed to further solidify how each technique may be leveraged with existing data. Attendees of this talk will discover several unconventional NLP tools such as:
- Scattertext for comparing word usage between two populations
- spaCy’s linguistic features to parse sentences by syntax
- DeepMoji for assigning emoji labels to short text

Bio: 

Kimberly Fessel is a Senior Data Scientist at Metis, the industry’s only accredited, full-time, immersive data science bootcamp. Prior to joining Metis as an instructor, Kimberly worked in digital advertising at MRM//McCann where she focused on helping clients understand their customers by leveraging unstructured data with modern NLP techniques. She holds a Ph.D. in applied mathematics from Rensselaer Polytechnic Institute and completed an NSF-funded postdoctoral fellowship in math biology at the Ohio State University. She is passionate about data visualization and about harnessing the power of language to tell compelling data stories.