Sentiment analysis using unsupervised learning for local government elections in South Africa

Abstract

Understanding public sentiment is vital for political parties in order for them to be able to structure their election campaigns around voter expectations. The study focuses on unsupervised learning to assess the variation of polarity sentiment in tweets during the 2021 South African local government election campaign. The study uses a pre-trained twitter-roberta-base-sentiment-latest model from Hugging Face and unsupervised lexicon based pre-trained approaches, namely: VADER and TextBlob to determine the polarity sentiment in order to gain insight that could be applied towards informing political campaigns and to see if there are any distinct sentiment patterns or shifts during different phases of the 2021 local government elections campaigns. Furthermore, the study applies the use of suspicious patterns and K-Means methods to classify the users as either bots and human using to be able to identify the user behind the keyboard. The study also make use of OpenAI GPT model to label the dataset for fine-tuning and addresses the issue of class imbalance. VADER and TextBlob results show a significant difference from that of the twitter-roberta-base-sentiment-latest models when comparing the statistical distribution based on the sentiment results and the user classification results. Based on the results, there is a significant variation across all sentiment classes and they vary over time. Furthermore, the results revealed TRBSL and TRBSL** outperforms VADER and TextBlob based on the scores for weighted accuracy and F1-scores. It was discovered that most of the tweets were generated by humans, with only few being identified as bot-generated and having a negative sentiments.