Text Mining for Marketing
Learn text mining techniques and applications for marketing analytics, from customer feedback analysis to brand monitoring and sentiment analysis.
Learn text mining techniques and applications for marketing analytics, from customer feedback analysis to brand monitoring and sentiment analysis.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning for Marketing Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
Tiếng Việt
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What you'll learn
Understand text mining applications in marketing
Analyze customer feedback using text mining
Implement sentiment analysis techniques
Develop brand monitoring strategies
Apply text mining for competitive analysis
Skills you'll gain
This course includes:
2.8 Hours PreRecorded video
36 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 12 modules in this course
This comprehensive course introduces text mining principles and applications in marketing contexts. Students learn various text mining techniques including sentiment analysis, topic modeling, and named entity recognition. The curriculum covers practical applications such as customer feedback analysis, brand monitoring, and competitive analysis, while addressing challenges in text mining implementation and ethical considerations.
Introduction to Text Mining for Marketing
Module 1 · 2 Hours to complete
Application of Text Mining in Marketing
Module 2 · 2 Hours to complete
Weekly Summative Assessment: Introduction and Application of Text Mining in Marketing
Module 3 · 1 Hours to complete
Text Mining Techniques for Marketing - I
Module 4 · 2 Hours to complete
Text Mining Techniques for Marketing - II
Module 5 · 3 Hours to complete
Weekly Summative Assessment: Text Mining Techniques for Marketing
Module 6 · 1 Hours to complete
Challenges - I
Module 7 · 1 Hours to complete
Challenges - II
Module 8 · 2 Hours to complete
Weekly Summative Assessment: Challenges of Text Mining Techniques
Module 9 · 1 Hours to complete
Future Directions
Module 10 · 1 Hours to complete
Implications
Module 11 · 2 Hours to complete
Weekly Summative Assessment: Future Directions and Implications
Module 12 · 1 Hours to complete
Fee Structure
Instructor
Assistant Professor in Information Technology and Systems at Jindal Global Business School.
Lalit Pankaj is an Assistant Professor in the Information Technology and Systems area at Jindal Global Business School, O.P. Jindal Global University, India. He teaches courses on Management Information Systems, Digital Transformation, Data Analytics, and Socio-Technical Systems. Lalit completed his PhD in Management Information Systems from the Indian Institute of Management Calcutta and holds a Master’s degree in Development Studies from the Tata Institute of Social Sciences, along with a B.Tech in Computer Science and Engineering from the Indian Institute of Technology Guwahati. With five years of diverse work experience, he initially worked in software development before serving as a Prime Minister’s Rural Development Fellow (PMRDF) for three years, focusing on innovative ICT for development projects in underdeveloped districts in India. His research interests include the dialectic relationship between humans and ICT, specifically in areas such as Sociomateriality, ICT and Development, ICT and Organizations, Social Media, Affordances, and IT Emergence.
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