BERT-based language models for US Elections, COVID-19, and analysis of the translations of the Bhagavad Gita
Date: 26 November 2021, Friday
Time: 4pm AEDT
Speaker: Dr Rohitash Chandra (UNSW Sydney)
Abstract:
There has been tremendous progress in language modelling with deep learning via long short-term memory (LSTM) models and variants such as bidirectional encoder representations from Transformers (BERT). Motivated by these innovations, in this seminar, we present three multi-disciplinary applications of BERT-based language models. Firstly, we develop a framework to model the US general elections. We investigate if sentiment analysis can provide a means to predict election outcomes. We use BERT-based language models for Twitter sentiment analysis leading to the US 2020 presidential elections. Secondly, we present a framework for sentiment analysis during the rise of novel COVID-19 cases in India. Finally, we compare selected translations (mostly from Sanskrit to English) of the Bhagavad Gita using semantic and sentiment analyses. We provide semantic analysis of selected chapters and verses across translations and provide directions for future work.
Bio:
Dr Rohitash Chandra is a Senior Lecturer in Data Science at the UNSW School of Mathematics and Statistics. Dr Chandra leads a program of research encircling methodologies and applications of artificial intelligence; particularly in areas of Bayesian deep learning, neuro-evolution, climate extremes, geoscientific models, and mineral exploration. Dr Chandra has developed novel methods for machine learning inspired by neural systems and learning behaviour that include transfer and multi-task learning, with the goal of modular deep learning. His current interest is uncertainty quantification and deep learning with applications to language models, vaccine research, and COVID-19. More information: https://research.unsw.edu.au/people/dr-rohitash-chandra
Link: https://au.bbcollab.com/guest/7e475d7ca5184d2b8bdee9b4aaee4761
Time: 4pm AEDT
Speaker: Dr Rohitash Chandra (UNSW Sydney)
Abstract:
There has been tremendous progress in language modelling with deep learning via long short-term memory (LSTM) models and variants such as bidirectional encoder representations from Transformers (BERT). Motivated by these innovations, in this seminar, we present three multi-disciplinary applications of BERT-based language models. Firstly, we develop a framework to model the US general elections. We investigate if sentiment analysis can provide a means to predict election outcomes. We use BERT-based language models for Twitter sentiment analysis leading to the US 2020 presidential elections. Secondly, we present a framework for sentiment analysis during the rise of novel COVID-19 cases in India. Finally, we compare selected translations (mostly from Sanskrit to English) of the Bhagavad Gita using semantic and sentiment analyses. We provide semantic analysis of selected chapters and verses across translations and provide directions for future work.
Bio:
Dr Rohitash Chandra is a Senior Lecturer in Data Science at the UNSW School of Mathematics and Statistics. Dr Chandra leads a program of research encircling methodologies and applications of artificial intelligence; particularly in areas of Bayesian deep learning, neuro-evolution, climate extremes, geoscientific models, and mineral exploration. Dr Chandra has developed novel methods for machine learning inspired by neural systems and learning behaviour that include transfer and multi-task learning, with the goal of modular deep learning. His current interest is uncertainty quantification and deep learning with applications to language models, vaccine research, and COVID-19. More information: https://research.unsw.edu.au/people/dr-rohitash-chandra
Link: https://au.bbcollab.com/guest/7e475d7ca5184d2b8bdee9b4aaee4761