  The age of social media has opened new opportunities for businesses. This
flourishing wealth of information is outside traditional channels and
frameworks of classical marketing research, including that of Marketing Mix
Modeling (MMM). Textual data, in particular, poses many challenges that data
analysis practitioners must tackle. Social media constitute massive,
heterogeneous, and noisy document sources. Industrial data acquisition
processes include some amount of ETL. However, the variability of noise in the
data and the heterogeneity induced by different sources create the need for
ad-hoc tools. Put otherwise, customer insight extraction in fully unsupervised,
noisy contexts is an arduous task. This research addresses the challenge of
fully unsupervised topic extraction in noisy, Big Data contexts. We present
three approaches we built on the Variational Autoencoder framework: the
Embedded Dirichlet Process, the Embedded Hierarchical Dirichlet Process, and
the time-aware Dynamic Embedded Dirichlet Process. These nonparametric
approaches concerning topics present the particularity of determining word
embeddings and topic embeddings. These embeddings do not require transfer
learning, but knowledge transfer remains possible. We test these approaches on
benchmark and automotive industry-related datasets from a real-world use case.
We show that our models achieve equal to better performance than
state-of-the-art methods and that the field of topic modeling would benefit
from improved evaluation metrics.
