In previous research on the development of the relationships between product attributes and customer satisfaction, the models did not adequately consider nonlinearity and the fuzzy emotions of customers in online reviews. Also, stable customer satisfaction was considered. However, customer satisfaction is changing with time rapidly, and a time-series analysis for customer satisfaction has not been conducted previously. To address these challenges, this study designed a novel methodology using adaptive neuro-fuzzy inference systems (ANFIS) in conjunction with Bi-objective particle swarm optimization (BOPSO) and sentiment analysis techniques. Sentiment analysis is employed to extract time-series customer satisfaction data from online reviews. Then, an ANFIS with the BOPSO method is proposed for the establishment of customer satisfaction models. In previous studies, ANFIS is an effective method to model customer satisfaction which can handle fuzziness and nonlinearity. However, when dealing with a large number of inputs, the modeling process may fail due to the complexity of the structure and the lengthy computational time required. Incorporating the BOPSO algorithm into ANFIS can identify the optimal inputs in ANFIS and effectively mitigate the inherent limitations of ANFIS. Using mobile phones as a case study, a comparison was performed between the proposed approach and another four approaches in modeling time-series customer satisfaction.