Discover patterns in marketing text without predefined outcome labels by combining topic modeling with text-derived network analysis. Learners build and inspect TF-IDF representations, select topic counts and membership assumptions, evaluate and interpret topic solutions, and construct directed or weighted networks from word co-occurrences and user mentions to identify central and bridging structures.

Unsupervised Text Classification for Marketing Analytics

Unsupervised Text Classification for Marketing Analytics
This course is part of Text Marketing Analytics Specialization


Instructors: Chris J. Vargo
Access provided by Charotar University of Science and Technology
Recommended experience
What you'll learn
Construct and inspect vocabulary and TF-IDF representations of a marketing corpus, and select and justify the number of topics and the use of hard or soft membership.
Tune topic-model parameters, preserve reproducible model outputs, and evaluate topic quality using within-topic similarity, between-topic separation, diagnostic sweeps, and exemplar documents.
Construct adjacency matrices and directed or weighted graphs from text and social-media interactions.
Interpret reciprocity, centrality, attributes, and bridge structures while documenting filtering and threshold choices.
Skills you'll gain
Tools you'll learn
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There are 3 modules in this course
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Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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University of Colorado Boulder

University of Colorado Boulder
