Towards Financial Sentiment Analysis in a South African Landscape
- URL: http://arxiv.org/abs/2106.10004v1
- Date: Fri, 18 Jun 2021 08:48:47 GMT
- Title: Towards Financial Sentiment Analysis in a South African Landscape
- Authors: Michelle Terblanche and Vukosi Marivate
- Abstract summary: 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.
Traditional lexicon-based and machine learning approaches are best suited to predict financial sentiment of news articles.
- Score: 0.015863809575305417
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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