Prediction of the ability and motivation to adopt Reproductive Health Behavioural change using anonymized customer center audio data

We share our methodology and findings from applying named entity recognition (NER) using machine learning, to identify behavioural patterns in transcribed, anonymized, privacy-preserving, and de-identified audio data from a customer contact centre data Nigeria based on the Fogg Behaviour Model (FBM). This work is part of a larger project that is focused on the practical application of AI to analyse and derive insight from large-scale data call centre data.The Fogg Behaviour Model (FBM) describes the interaction of three key elements (Motivation, Ability and a Prompt) and their interaction to produce behavioural change in relation to the adoption of positive reproductive health behaviour. This work is part of a larger project that is focused on the practical application of artificial intelligence to analyse and derive programmatic insight from large-scale customer contact centre audio data. The entity recognition model called Fogg Model Entity Recognition (FMER) was trained using spaCy, an open source software library for advanced natural language processing, on a total of 11510 words and scored an F1 of 98.5