## 1 Introduction  
Social media platforms such as Twitter (now X), Facebook (now Meta), YouTube, and TikTok are critical in today's digital age for consumers to share experiences and engage with their communities. In this current data-driven economy, analysing consumer data from social media is vital for stakeholders such as investors, regulators, and financial institutions to grasp societal viewpoints on financial products and services. Messages or comments posted on social media are valid tools that facilitate real-time communication, engagement, and connection with a large global audience. Analysing these data provides valuable insights for businesses, marketers, and researchers interested in understanding public opinions and trends. In South Africa, the penetration of social media in the hospitality sector accounts for 67% of all tourism, while the wholesale, retail, catering, and accommodation sectors contribute 14.4% to the country's GDP [1]. In comparison to the neighbouring country, Zimbabwe, the Digital 2022 report recorded that there were 4.65 million internet users in Zimbabwe in January 2022 and 1.55 million social media users, which translates to 10.2% of the total population [2]. Moreover, social media are a key source of information for citizens and activists in Zimbabwe. In Northern Africa, approximately 40.4% of the population utilises social media, compared to 41.6% in Southern Africa [3]. In Central Africa, just under 10% of the population engages with social media, marking the lowest rate in Africa and globally [4]. A study by Macro-Monitor in 2022 [5] identified comparable patterns in the extent to which American households, homeowners, and business professionals utilise social media for information exchange on financial institutions. The study found that 52% of respondents born in the US after 1998 shared information on social media. This proportion decreases as households age drops to 16% when the household owner was born before 1960. Sentiment analysis is crucial to understanding the users' post or comments, which comprise text, emojis, and hashtags, which can provide valuable information about the user's polarity towards specific needs, topics, or events. Sentiment analysis requires data labelling for the training of the machine learning (ML) algorithms. These data labels should be of high quality so that the ML classifier can be closer to the ground truth. One popular method to obtain the gold standard labelled dataset is using crowdsourcing or workers for their annotation [6; 7]. However, the use of crowdsourcing for data annotation offers a more affordable solution, allows a variety of perspectives and experiences, and is ultimately very scalable. In contrast, the accuracy and consistency of the annotations can vary widely with low-quality annotations and lead to misinterpretations and errors; e.g., there is no contextual understanding. Amazon Mechanical Turk (MTurk) [8] is another common way to generate a high-quality labelled dataset. This approach is less reliable and accurate for understanding context, annotating text, and learning word dependencies. Additionally, because online social media texts are rich in slang, emojis, hashtags, idioms, and acronyms, using the MTurk approach will produce low-quality annotations. Although the cost of using MTurk may be very low, the time needed to develop the interface and tackle the spammer problem is not negligible; validation and correction costs to ensure minimal quality are very high [9]. Lexicon-based methods (LBMs) are often employed to determine sentiment in text since they are simpler and faster than supervised learning approaches. Popular lexicon-based methods include VADER, SentiWordNet, and LIWC [10; 11]. For example, VADER is a rule-based method that is efficient and beats other approaches to social media data, particularly Twitter (now X). It also considers emojis when calculating sentiment scores.