<![CDATA[Background: This study, based on the phenomenon that consumer reviews reflect perceptions of product quality, analyzes big data from user reviews using the Theory of Planned Behavior framework. This study is useful for both academics and practitioners in uncovering consumer behavior based on big data from user reviews. Objective: This study aims to analyze consumer behavior based on sentiment big data using the Theory of Planned Behavior framework. Method: This study employs a quantitative approach by analyzing user sentiment. The dataset consists of 7,000 BRImo reviews from the Play Store. Data analysis is based on a lexical approach, in which the data is categorized into positive, negative, and neutral labels. Subsequently, the data is analyzed using the Theory of Planned Behavior. Results: This study yielded a variety of sentiment analysis results, with negative sentiment at 83.9%, positive sentiment at 12.2%, and neutral sentiment at 3.9%. This indicates that the majority of BRImo users expressed negative sentiment, with reviews focusing on login difficulties, app performance, and the presence of bugs. Furthermore, model evaluation showed a high level of accuracy based on KNN and Confusion Matrix analysis. Based on a theoretical approach, reviews that are mostly negative indicate a perception of dissatisfaction and a lack of consumer trust in BRI’s financial platform services. Conclusion: The findings of this study provide insights into the integration of sentiment analysis with consumer behavior approaches. This study offers both academic and practical implications for maintaining customer satisfaction through high-quality service systems. Keywords: Sentiment Analysis; Big Data; Consumer Behavior; Mobile Banking; Theory of Planned Behavior]]>
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