Social Media Mining with Natural Language Processing

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2015-11-06 (Creation date: 2015-11-06)
Main contributors
Abdul-Mageed, Muhammad; Dickinson, Markus
With the increasing role social media platforms like Facebook, Twitter, YouTube, and Tumbler play in our lives today, the body of data generated by their users continues to grow phenomenally. Accordingly, searches and processing of social media data beyond the limiting level of surface words are becoming increasingly important to business and governmental bodies, as well as to lay web users. Detection of sentiment, emotion, deception, gender, sarcasm, age, perspective, topic, community, and personality are all valuable social meaning components that promise to be important elements of next generation search engines and web intelligence. The emerging area of extracting social meaning from social media data using computational methods is known as Social Media Mining (SMM). 

This workshop is intended to first introduce the core ideas of natural language processing (NLP) and then to provide the ideas and some hands-on instruction in mining social data using NLP and machine learning technologies. As such, we will address practical issues related to building tools to mine social media data and some of the primary computational methods employed for modeling social meaning as occurring in these data. 

Indiana University Workshop in Methods
Workshop in Methods
Social Science Research Commons
Related Item
Accompanying presentation materials on IUScholarWorks 


Muhammad Abdul-Mageed is a Visiting Assistant Professor in the School of Informatics and Computing. Muhammad's interests are at the intersection of machine learning, natural language processing, and social media. He is especially interested in creating more 'social' machines.
Markus Dickinson is an Associate Professor of Linguistics. Part of his research focuses on the intersection of linguistic annotation and natural language processing and the other part focuses on the automatic analysis of second language learner data.

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