Insights From Textual Data and Machine Learning Algorithms For Consumer Behavior
People generate vast amounts of text data as they search for information on the Internet, share articles, and write reviews. The existence of such data, along with advances in machine learning, have created new opportunities for scientists.Our session explores the use of these data/methods for topics central to consumer behavior.
Citation:
Ada Aka, Sudeep Bhatia, Michael Yeomans, Alison Wood Brooks, Ishita Chakraborty, Minkyung Kim, K. Sudhir, Vincent Chen, Zhihao Zhang, and Ming Hsu (2021) ,"Insights From Textual Data and Machine Learning Algorithms For Consumer Behavior", in NA - Advances in Consumer Research Volume 49, eds. Tonya Williams Bradford, Anat Keinan, and Matthew Matthew Thomson, Duluth, MN : Association for Consumer Research, Pages: 774-779.
Authors
Ada Aka, University of Pennsylvania
Sudeep Bhatia, University of Pennsylvania
Michael Yeomans, Imperial College London
Alison Wood Brooks, Harvard University
Ishita Chakraborty, Yale School of Management
Minkyung Kim, University of North Carolina-Chapel Hill
K. Sudhir, Yale School of Management
Vincent Chen, University of California, Berkeley
Zhihao Zhang, University of California, Berkeley
Ming Hsu, University of California, Berkeley
Volume
NA - Advances in Consumer Research Volume 49 | 2021
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