CROSS-CULTURAL EMOTION ANALYSIS ON X USING BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS (BERT)

Abstract

It is essential to understand how environmental factors and cultural backgrounds affect emotional responses to build Human-Computer Interaction (HCI) technologies. This study uses the BERT (Bidirectional Encoder Representations from Transformers) model to investigate the emotional landscape of interactions on the X platform across various cultural contexts. The aims of the study are as follows: (1) Gather and prepare X platform data from COVID-19 sources in India, Pakistan, Malaysia, the US, the UK, Australia, and Canada. (2) Use a BERT model to qualitatively analyze tweet sentiment. (3) Assess the accuracy of the model and look for sentiment trends in tweets from nations throughout the pandemic. The BERT model successfully classified sentiment, as seen by its 80.29% accuracy rate on test data. Sentiment research showed that positive sentiment was far more prevalent in the US, Canada, and Australia, indicating that these countries were better able to adjust to the COVID-19 situation. Stability was seen in the balanced sentiment distributions displayed by Pakistan, India, and the United Kingdom. Despite having fewer data points, Pakistan and Malaysia continued to have largely positive attitudes. This study provides the basis for a comparative analysis of emotional responses by taking contextual and cultural aspects into account.