A Scoping Review of Sentiment Analysis for Customer Satisfaction in Digital Banking: Mapping the Research Gap Toward Multimodal Integration

Sentiment analysis has become increasingly important for interpreting customer-generated content across social media and digital technologies. However, research on digital banking continues to rely predominantly on textonly data, with limited attention to multimodal integration. This scoping review mapped the literature on sentiment analysis for customer satisfaction in digital banking, identified research gaps, and outlined future directions. The review followed the PRISMA-ScR guidelines and searched six databases—ScienceDirect, IEEE Xplore, Wiley Online Library, ACM Digital Library, SpringerLink, and Google Scholar—for English-language studies published from 2015 to 2026; the final search was conducted on May 10, 2026. Of 1,631 records identified, 35 studies met the inclusion criteria. Text only data were used in 31 studies (88.6%), multi-source text was used in one study (2.9%), text combined with survey data in two studies (5.7%), and numerical time-series data in one study (2.9%); no included study integrated text, audio, and video within a single sentiment analysis framework. The literature was geographically concentrated and relied mainly on traditional machine-learning and hybrid approaches, whereas transformer/LLM based methods, explainable AI, and real-time approaches remained limited. The absence of banking-specific multimodal datasets is a key barrier to progress. Future research should prioritize privacy-preserving multimodal datasets, multilingual and culturally diverse models, explainable AI, and real-time deployment to support more reliable assessment of customer experience in digital banking.