In the age of social media, customers have become opinion makers that share their experience. People interested in a product can reach for these reviews on the whole Internet, thus leading to heterogeneity of data sources. As the massive amounts of data are not systematically accessible through APIs, companies frequently scrap it from several platforms and marketplaces. Data acquisition processes include some amount of ETL, yet the variability of noise contained in the data and the heterogeneity induced by different existing sources create the need for adhoc tools. In other words, and even if large quantities of data are accessible for virtually free, customer insight extraction is an harduous task. To circumvent this issue, we apply the Embedded Dirichlet Process (EDP) and the Embedded Hierarchical Dirichlet Process (EHDP) in a realistic setting regarding industrial practices. These neural topic models simultaneously learn the number of topics, representations of documents, topic embeddings and word embeddings from data. These properties allow for semantic units' disambiguation and for deeper text exploration than other approaches. As such, they can also serve as a way to refine industrial ETL processes. When not higher, both the EDP and the EHDP models achieve similar likelihood than other methods in our experimental settings, while providing with more analytical levels. Last but not least, we could achieve our results without having to perform costly reruns to find the number of topics.