Forecasting the box offices of movies is indispensable, whether it is for the stock investment decisions of the movie company, the movie sales strategy design of the advertising company, or the movie screening schedules of cinemas. However, this task is very challenging because it is affected by many complicated factors. In view of this complexity, we propose a method for forecasting the box offices of movies coming soon based on social media analysis. First, movie-related online reviews and their numbers are collected from microblogs. Second, several important features, i.e., the sentiment value of microblog texts, the consumers’ RFM value and the degree of moviegoers’ attention, are extracted or calculated. Subsequently, we use the extracted features to expand the parameters of the Bass model, and build two improved Bass models for forecasting the box offices of movies in the initial week and during the diffusion cycle, respectively. Finally, through fitting data progressively, we realize the forecasting task. The experimental results show that compared with the traditional forecast models and existing studies, the improved Bass method proposed in this paper has better performance, overall.