Service Quality Evaluation in Electronic Invoicing: Sentiment Analysis of Customer Behavior

Electronic invoicing has become a fundamental part of the digital transformation of organizations, facilitating operational efficiency. This study evaluates the quality of service in electronic invoicing systems in Ecuador through sentiment analysis applied to user comments. Using advanced natural language processing (NLP) techniques, the BERT model was implemented to automatically classify customer opinions into positive, negative, or neutral sentiments. The analysis was conducted in four stages: data processing, sentiment polarity classification, comment categorization (into recommendations and non-recommendations), and result interpretation. The findings reveal thematic patterns that reflect both user-valued elements and sources of dissatisfaction, particularly in terms of usability, technical support, and system performance. The results show a predominance of positive sentiment, while also highlighting critical areas that need improvement. The model demonstrated high accuracy and enabled real-time visualization of customer perception. As a recommendation, the study proposes optimizing the user interface, automating key processes, and providing staff training to enhance the user experience and strengthen service competitiveness within the digital sector.