This paper aims to conduct an exploratory research that proposes an idea generation model, named IGMCC, to develop innovative ideas from consumers’ complaint messages. The model utilizes text mining and classification techniques to derive outputs. Data was collected from 2018/11/1 to 2019/4/30 in the domain of mobile phone from an online forum of a company in Taiwan. The collected dataset contained valid 2406 message records related to consumers’ complaints about the products and services. The sparse terms were removed and produced term matrix with occurrence frequency was discretized to be used by the classification-oriented data mining algorithm. The results showed that 33 classification rules were obtained and the prediction accuracy is 77.1%. Discussion and implications are addressed.