Sharifian-Attar, Vida;
De, Suparna;
Jabbari, Sanaz;
Li, Jenny;
Moss, Harry;
Johnson, Jon;
(2023)
Analysing Longitudinal Social Science Questionnaires: Topic modelling with BERT-based Embeddings.
In:
2022 IEEE International Conference on Big Data (Big Data).
IEEE: Osaka, Japan.
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Abstract
Unsupervised topic modelling is a useful unbiased mechanism for topic labelling of complex longitudinal questionnaires covering multiple domains such as social science and medicine. Manual tagging of such complex datasets increases the propensity of incorrect or inconsistent labels and is a barrier to scaling the processing of longitudinal questionnaires for provision of question banks for data collection agencies. Towards this effort, we propose a tailored BERTopic framework that takes advantage of its novel sentence embedding for creating interpretable topics, and extend it with an enhanced visualisation for comparing the topic model labels with the tags manually assigned to the question literals. The resulting topic clusters uncover instances of mislabelled question tags, while also enabling showcasing the semantic shifts and evolution of the topics across the time span of the longitudinal questionnaires. The tailored BERTopic framework outperforms existing topic modelling baselines for the quantitative evaluation metrics of topic coherence and diversity, while also being 18 times faster than the next best-performing baseline.
| Type: | Proceedings paper |
|---|---|
| Title: | Analysing Longitudinal Social Science Questionnaires: Topic modelling with BERT-based Embeddings |
| Event: | 2022 IEEE International Conference on Big Data (Big Data) |
| Dates: | 17 Dec 2022 - 20 Dec 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/bigdata55660.2022.10020678 |
| Publisher version: | https://doi.org/10.1109/BigData55660.2022.10020678 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Analytical models, Computational modeling, Social sciences, Semantics, Data visualization, Manuals, Coherence |
| UCL classification: | UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10165551 |
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