Relevance & Research Question
As a way to reduce survey length, or even to replace surveys, we have been involved in passive data (web navigation) collection for a couple of years. Passive data is relevant to the description of online behaviour, but declarative data is still needed to interpret and explain behavior; as one could hope to directly infer individual attitudes from internet behavior. Due to the recent advances in natural language, such automated analysis of contents and attitudes is no longer an elusive dream. As an experiment, we have thus used BERT (Google's deep learning based language model) and further proprietary deep learning techniques in order to try and analyze opinions, based on online media consumption.
More precisely, is it possible to instantly, without asking anything, combine passive data and BERT and get a deep understanding of the audience of any website? For example, is it possible to get the specific attitude of visitors to Audi.com towards ecology? Our talk will be based on the presentation of a prototype of an online tool/dashboard. Its objective will be to share the promises and the challenges of this usage of AI.
Methods and Data
The data we use is based on 7000 respondents (from France, Germany, UK) who agreed to install a tracking software. For 347 days on average, we continuously collected the navigation data (urls visited and / or apps used) for each of them. Data is analyzed via BERT properly trained. Realtime vizualisation of the results powered by Tableau.
Results
The quality of results relies on the ability of our neutral network to accurately categorize words in a consistent semantic field. Some results are pretty impressive: without having been trained on these particular fields, “Ronaldo” is associated to football and “parenting” is related to family life. But some are disappointing: “psoriasis” is associated to medicine in general (not even to dermatology only). We will discuss these results and will try to explain them.
Added Value
It is a work in progress, at this stage, the main benefit is to present and discuss concrete applications of AI in market research.