Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Towards Financial Sentiment Analysis in a South African Landscape

Domain:

natural language processing

Record type:

paperdataset
Creator:
TerMarivate, Vukosi
Publisher:
arXiv
Host:avatar
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. Accepted for publication in Proceedings of CD-MAKE 2021 Conference

Visit

doi.orgarxiv.org

Tasks

sentiment analysistext classification

Tags

Computation and Language (cs.CL)FOS: Computer and information sciencesFOS: Computer and information sciences

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similar

nblomerus/South-African-Bank-Sentiment-AnalysisSentiment Analysis of South African News CompanyTowards Mitigating Cyberfraud in the South African Financial Institutions: A Deep Learning ApproachSlyth3/Sentiment-analysis-of-South-African-Banks-POCTriLex: A Framework for Multilingual Sentiment Analysis in Low-Resource South African Languagesziqubusibusiso-svg/Sentiment-Analysis-on-South-African-Twitter-Data

nblomerus/South-African-Bank-Sentiment-Analysis

Investigation of the sentiment of the top South African banks # Twitter Analysis of Popular South A

Sentiment Analysis of South African News Company

Towards Mitigating Cyberfraud in the South African Financial Institutions: A Deep Learning Approach

This study demonstrates the application of deep learning approach specifically the deep learning for

Slyth3/Sentiment-analysis-of-South-African-Banks-POC

Sentiment Analysis on the top 4 banks in South Africa # Bank Sentiment Analysis POC This project is

TriLex: A Framework for Multilingual Sentiment Analysis in Low-Resource South African Languages

Low-resource African languages remain underrepresented in sentiment analysis, limiting both lexical

ziqubusibusiso-svg/Sentiment-Analysis-on-South-African-Twitter-Data

Predicting Xenophobic Attacks: Sentiment Analysis on South African Twitter Data # Sentiment-Analysi