The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today's challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence ABOUT THE AUTHORS Raphael Kwaku Botchway is a Ph.D. candidate with research interest in Soft Computing, Social Media and Text Analytics at the Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic. His research results have been presented at numerous reputable international conferences. Abdul Bashiru Jibril is a Doctoral Researcher at the Department of Management and Marketing at TBU in Zlin. He is particularly interested in deploying data mining techniques in extracting intelligence to improve business decision making. The results of his research have been published in peer-reviewed scientific journals and presented at numerous international conferences.

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Technology has sped up the innovation effort in the automobile industry. Further to this automobile innovation such as intelligent climate control, adaptive cruise control, and others, we find in today's vehicles, it has been predicted that by 2030, there will be driverless vehicles, of which samples are already on the market. The news and the sights of these so-called driverless vehicles have generated mixed reactions, and this motivated our study. Hence the present study focuses on a dataset of tweets associated with driverless vehicles downloaded using the Twitter API. Valence Aware Dictionary and sentiment Reasoner (VADER), a lexicon and rule-based sentiment analysis tool were used in extracting sentiments on the tweets to gauge public opinions about the acceptance and adoption of the driverless vehicles ahead of their launch. The VADER sentiment analysis results, however, show that the general discussion on driverless vehicles was positive. Besides, we generated a word cloud to visually analyze the terms in the dataset to gain further insights and understand the messages conveyed by the tweets in other to enhance the usage and adoption of driverless vehicles. This study will enable self-driving vehicle technology service providers and autonomous vehicle manufacturers to gain more insights on how to transform the transportation sector by investing in research and technology.

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