Since market is constantly changing, companies need to continuously adjust the pace to get a head start. Forecasts are essential to the business’s decision making and planning processes. Better forecasting can contribute to better price structuring and better inventory management. However, it is a challenging problem owing to the volatility of demand which depends on many factors. And the situation is prominent in fashion product due to its sales versatility. Past research shows that disseminating information through word-of-mouth communication is one of the most effective mediums for relaying important product and company information. It not only plays an important role in the evaluation of products but also plays an important role in society as well. Although many companies have found its effectiveness through lots of literature reviews, most of them are limited in focusing on the the frequency and types of word-of-mouth behavior, or the effects of word-of-mouth behavior on product evaluation. Few of them discuss the relationship with sales volume. In this study, an automatic mining approach is proposed to resolve the aforementioned issues. According to this method, a text mining technique and Naive Bayes classifier will be used to determine the rating of each product-related article extracted from the Internet. Based on the regression model, some target functions have been designed to clarify the relationship between the rating of world-of-mouse and the sales. And a valid forecasting method is generated with the smallest prediction error. Performances of our model are evaluated by using real data from a cosmetic retailer in Taiwan. The experimental results demonstrate that this model is especially suitable for the fashion product with sufficient discussion on the Internet. In addition, our proposed method is proved to outperform several traditional sales forecasting methods such as moving average, exponential smoothing and exponential smoothing with trend. Therefore, we believe that this model can effectively enhance the prediction accuracy when applied to fashion products.