With the emergence of the internet, customers have become far more than mere consumers: they are now opinion makers. As such, they share their experience of goods, services, brands, and retailers. People interested in a certain product often reach for these opinions on all kinds of channels with different structures, from forums to microblogging platforms. On these platforms, topics about almost everything proliferate, and can become viral for a certain time before they begin stagnating, or extinguishing. The amount of data is massive, and the data acquisition processes frequently involve web scraping. Even if basic parsing, cleaning, and standardization exist, the variability of noise create the need for ad-hoc tools. All these elements make it difficult to extract customer insights from the internet. To address these issues, I propose to devise time-dynamic, nonparametric neural-based topic models that take topic, document and word linking into account. I also want to extract opinions accordingly with multilingual contexts, all the while making my tools relevant for pretreatment improvement. Last but not least, I want to devise a proper way of evaluating models so as to assess all their aspects.