Descrizione Progetto
Dataset
Our dataset includes 1115 tweets. Following the definition of the variables of interest for each tweet , we processed the scraped tweets to remove emoticons, links, and any other potential
disruptive elements for sentiment analysis. Furthermore, we excluded tweets consisting of fewer than five words and manually removed the tweets that were clearly generated by bot platforms
to strengthen our analysis.
Methodology and Results
This study examines how the interaction between access to credit and new fintech ventures evolved during and after the COVID-19 pandemic by conducting three different lexicon-based
sentiment analyses using NLTK, TextBlob, and Flair Python libraries. We previously gathered data from Twitter (subsequently rebranded as X) by applying different combinations of
keywords in our scraper script to better understand the phenomenon and enhance the quality of the final dataset. We defined the most appropriate set of keywords that we subsequently used for analysis. We also empirically estimated whether the results obtained could be generalized to the continents involved
Highlights
• The study reveals a slight downward trend in sentiment regarding credit access and fintech since the COVID-19 pandemic, emphasizing the importance of using targeted keywords to extract
relevant insights for policymakers and industry stakeholders.
• Semantic analysis highlights key sectors impacted by fintech innovations (e.g., banking, finance, startups) and associated technologies (e.g., data science, analytics), suggesting potential avenues for future fintech developments
