Purpose: This study aims to establish a comprehensive satisfaction analysis framework by mining online restaurant reviews, which can not only accurately reveal consumer satisfaction but also identify factors leading to dissatisfaction and further quantify improvement opportunity levels. Design/methodology/approach: Adopting deep learning, Cross-Bidirectional Encoder Representations Transformers (BERT) model is developed to measure customer satisfaction. Furthermore, opinion mining technique is used to extract consumers’ opinions and obtain dissatisfaction factors. Furthermore, the opportunity algorithm is introduced to quantify attributes’ improvement opportunity levels. A total of 19,133 online reviews of 31 restaurants in Universal Beijing Resort are crawled to validate the framework. Findings: Results demonstrate the superiority of Cross-BERT model compared to existing models such as sentiment lexicon-based model and Naïve Bayes. More importantly, after effectively unveiling customer dissatisfaction factors (e.g. long queuing time and taste salty), “Dish taste,” “Waiters’ attitude” and “Decoration” are identified as the three secondary attributes with the greatest improvement opportunities. Practical implications: The proposed framework helps managers, especially in the restaurant industry, accurately understand customer satisfaction and reasons behind dissatisfaction, thereby generating efficient countermeasures. Especially, the improvement opportunity levels also benefit practitioners in efficiently allocating limited business resources. Originality/value: This work contributes to hospitality and tourism literature by developing a comprehensive customer satisfaction analysis framework in the big data era. Moreover, to the best of the authors’ knowledge, this work is among the first to introduce opportunity algorithm to quantify service improvement benefits. The proposed Cross-BERT model also advances the methodological literature on measuring customer satisfaction.