Support Vector Machine for Sentiment Analysis of Nigerian Banks Financial Tweets

Abstract

The rise of social media paves the way for unprecedented benefits or risks to several organisations depending on how they adapt to its changes. This rise comes with a great challenge of gaining insights from these big data for effective and efficient decision-making that can improve quality, profitability, productivity, competitiveness and customer satisfaction. Sentiment analysis is the field that is concerned with the classification and analysis of user-generated text under defined polarities. Despite the upsurge of research in sentiment analysis in recent years, there is a dearth of literature on sentiment analysis applied to banks’ social media data and mostly on African datasets. Against this background, this study applied a machine learning technique (support vector machine) for sentiment analysis of Nigerian banks’ Twitter data within 2 years, from 1st January 2017 to 31st December 2018. After crawling and preprocessing the data, the LibSVM algorithm in WEKA was used to build the sentiment classification model based on the training data. The performance of this model was evaluated on a pre-labelled test dataset generated from the five banks. The results show that the accuracy of the classifier was 71.8367%. The precision for both the positive and negative classes was above 0.7, the recall for the negative class was 0.696 and that of the positive class was 0.741 which shows the prediction did better than chance in addition to other measures. Applying the model to predicting the sentiments of the five Nigerian banks Twitter data reveals that the number of positive tweets within this period was slightly greater than the number of negative tweets. The scatter plots for the sentiments series indicated that the majority of the data falls between 0 and 100 sentiments per day, with few outliers above this range.

Faithful Chiagoziem OWNUEGBUCHE

PhD Candidate in Machine Learning and Blockchain Technology

My research focuses on the intersection of machine learning and blockchain technology, particularly their applications in the fields of cybersecurity and finance.