The expansion of the Internet has led to a huge amount of information posted by consumers online through social media platforms such as forums, blogs, and product reviews. This study proposes a diffusion model that accommodates pre-launch social media information and combines it with post-launch sales information in the Bass model to improve the accuracy of sales forecasts. The model is characterized as the extended Bass model, with time varying parameters whose evolutions are affected by the consumer’s communications in social media. Specifically, we construct variables from social media by using sentiment analysis and topic analysis. These variables are fed as key parameters in the diffusion model’s evolution process for the purpose of plugging the gap between the time-invariant key parameter model and that of observed sales. An empirical study of the first-generation iPhone during 2006 and 2007 shows that the model using additional variables extracted from sentiment and topic analysis on BBS performs best based on several criteria, including DIC (Deviance Information Criteria), marginal likelihood, and forecasting errors of holdout samples. We discuss the role of social media information in the diffusion process for this study.