Financial sentiment analysis : an NLP approach towards reputation management

Abstract

Sentiment analysis as a sub- eld 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 study contributed to updating an existing sentiment dictionary and developing a full pipeline to filter data for financial topics and predict sentiment. Using a binary logistic regression model and a binary XGBoost classifier on both headlines and article content produced accuracies of >85%. The predicted sentiments correlated quite well with share price and highlighted the potential use of sentiment as an indicator of financial performance. 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.