<p>Survival analysis problems are crucial in business area. Most of the existing research conducts business survival problem with structured data, yet overlooks the potential rich information provided in text data. Therefore, we creatively extract useful information from online reviews and study the influence of textual features on the occurrence of a business event. We propose a novel dynamic survival topic model (DSTM) that first extract topic proportions from text corpus as topic features in each time slice, then estimate the coefficients each feature contributes to the hazard ratio under time-dependent circumstances. Experiments on two real-word business datasets show that our proposed model can not only identify the true event time in the corpus and how the features influence the event, but also outperform two baseline models in six evaluation metrics. Our findings provide significant implications for decision makers to understand users’ personal attitude towards corresponding event behind their corpus and personal features.</p>