Opinion Mining Using Population-tuned Generative Language Models
Allmin Susaiyah Abhinay Pandya Aki Härmä
We present a novel method for mining opinions from text collections using
generative language models trained on data collected from different
populations. We describe the basic definitions, methodology and a generic
algorithm for opinion insight mining. We demonstrate the performance of our
method in an experiment where a pre-trained generative model is fine-tuned
using specifically tailored content with unnatural and fully annotated
opinions. We show that our approach can learn and transfer the opinions to the
semantic classes while maintaining the proportion of polarisation. Finally, we
demonstrate the usage of an insight mining system to scale up the discovery of
opinion insights from a real text corpus.