A Topical Approach to Capturing Customer Insight In Social Media

TL;DR

This paper introduces three unsupervised, nonparametric models based on Variational Autoencoders for extracting customer insights from noisy social media data, outperforming existing methods.

Contribution

It proposes novel Variational Autoencoder-based approaches for fully unsupervised topic modeling in noisy social media data, with no need for transfer learning.

Findings

Models achieve equal or better performance than state-of-the-art methods.

Approaches effectively handle noisy, heterogeneous social media data.

Field benefits from improved evaluation metrics for topic modeling.

Abstract

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.