Although large amounts of data are now available to companies, mere possession of these datais not sufficient, and, for better business decisions, it is necessary to perform thorough data analysis.Nowadays, Social Networks Services (SNS) have become important data sources. The rapid growth of SNShas led to their wide use in various research trends in social sciences. In the present study, we aim to enhancethe current understanding of the possibilities offered by social data for brand communication analysis in thefinancial sector. To this end, a traditional methodology and a digital methodology are used to investigate thebrand image of financial entities. The traditional methodology is the Periodic Evaluation of the Image (PEI).The digital methodology is sentiment analysis, a machine learning technique for Big Data analytics in socialsciences using an algorithm developed in Python. The data are analyzed using both methodologies, and thentheir results are compared. The findings suggest that, while the results obtained using the method based onBig Data are consistent with the results obtained with the traditional methodology, the former method allowsfor an easier and faster data analysis. The limitations of the present study relate to the size of the sample, thestudied sector, and the scope of reviewed literature.
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