Prediction of the Ability and Motivation to Adopt Reproductive Health Behavioural Change Using Anonymized Customer Center Audio Data
Abstract
In this work, we share our methodology and findings from applying named entity recognition (NER) using machine learning to identify behavioural patterns in transcribed family planning client call centre data in Nigeria based on the Fogg’s model. The Fogg Behaviour Model (FBM) describes the interaction of three key elements: Motivation (M), Ability (A), and a Prompt (P) and their interaction to produce behavioural change. This work is part of a larger project that is focused on practical application of artificial intelligence to analyse and derive insight from large scale data call centre 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 F1 score of 98.5
Cite
Text
Adekanmbi and Soronnadi. "Prediction of the Ability and Motivation to Adopt Reproductive Health Behavioural Change Using Anonymized Customer Center Audio Data." ICLR 2023 Workshops: AfricaNLP, 2023.Markdown
[Adekanmbi and Soronnadi. "Prediction of the Ability and Motivation to Adopt Reproductive Health Behavioural Change Using Anonymized Customer Center Audio Data." ICLR 2023 Workshops: AfricaNLP, 2023.](https://mlanthology.org/iclrw/2023/adekanmbi2023iclrw-prediction/)BibTeX
@inproceedings{adekanmbi2023iclrw-prediction,
title = {{Prediction of the Ability and Motivation to Adopt Reproductive Health Behavioural Change Using Anonymized Customer Center Audio Data}},
author = {Adekanmbi, Olubayo and Soronnadi, Anthony},
booktitle = {ICLR 2023 Workshops: AfricaNLP},
year = {2023},
url = {https://mlanthology.org/iclrw/2023/adekanmbi2023iclrw-prediction/}
}