Grad Seminar Guest Talk: Raghava Rao Mukkamala on 1/12

Thursday, Jan. 12, 4:30-5:30, POST 126

Multi-Dimensional Text Analytics: Concepts, Methods, Tools and Findings

Dr. Raghava Rao Mukkamala
Centre for Business Data Analytics
Copenhagen Business School, Denmark

Abstract:

In this talk, I first present a methodology for multi-dimensional text analysis using text mining, topic modeling, and domain-specific classification. Second, I present a research tool, MUTATO developed at the Centre for Business Data Analytics (http://bda.cbs.dk) for performing text mining, topic modeling and text classification using supervised and unsupervised machine learning approaches. MUTATO is developed as a web application by using Python and C# programming languages with open-source libraries such as Natural Language Toolkit (NLTK) and Gensim topic modeling libraries. As part of unsupervised approaches, MUTATO supports text mining by providing keyword analysis, collocation analysis and word-frequency analysis. MUTATO supports topic modeling to identify/discover topics and hidden information patterns from a given text corpus. As part of supervised machine-learning approaches, MUTATO supports 1) manual coding of text documents for preparing training sets using a systematic approach for manual content analysis 2) classification of text corpus according the given domain-specific models using the training sets. MUTATO also provides key performance measures for text classification in terms of precision, recall, accuracy and F-measure. Third and last, I will present applications of MUTATO in different research domains, highlight key empirical findings, discuss limitations and outline future work.

Bio:

Raghava Rao Mukkamala is an Assistant Professor of Computational Social Science at the Department of IT Management, Copenhagen Business School; external lecturer of applied computing at the Westerdals Oslo School of Arts Communication and Technology; and co-director of the Computational Social Science Laboratory (cssl.cbs.dk). Raghava’s current research focus is on interdisciplinary approach to big data analytics. Combining formal/mathematical modeling approaches with data/text mining techniques and machine learning methodologies, his current research program seeks to develop new algorithms and techniques for big data analytics such as Social Set Analytics.  Raghava holds a PhD degree in Computer Science and a M.Sc degree in Information Technology, both from IT University of Copenhagen, Denmark and a Bachelor of Technology degree from Jawaharlal Nehru Technological University, India. Before moving to research, Raghava has many of years of programming and IT development experience from Danish IT industry.