Towards Financial Sentiment Analysis in a South African Landscape

TL;DR

This paper investigates financial sentiment analysis in South Africa, finding traditional methods outperform pre-trained models, with sentiment correlating well with share prices, and updates an existing sentiment dictionary.

Contribution

It introduces an updated financial sentiment dictionary tailored for South African data and evaluates traditional approaches over pre-trained models.

Findings

Traditional lexicon-based and machine learning methods achieved 84-94% accuracy.

Pre-trained sentiment analyzers were less effective for South African financial news.

Sentiment predictions showed strong correlation with share prices.

Abstract

Sentiment analysis as a sub-field of natural language processing has received increased attention in the past decade enabling organisations to more effectively manage their reputation through online media monitoring. Many drivers impact reputation, however, this thesis focuses only the aspect of financial performance and explores the gap with regards to financial sentiment analysis in a South African context. Results showed that pre-trained sentiment analysers are least effective for this task and that traditional lexicon-based and machine learning approaches are best suited to predict financial sentiment of news articles. The evaluated methods produced accuracies of 84\%-94\%. The predicted sentiments correlated quite well with share price and highlighted the potential use of sentiment as an indicator of financial performance. A main contribution of the study was updating an existing sentiment dictionary for financial sentiment analysis. Model generalisation was less acceptable due to the limited amount of training data used. Future work includes expanding the data set to improve general usability and contribute to an open-source financial sentiment analyser for South African data.