دانلود رایگان مقاله پیش بینی فروش محصول با بررسی آنلاین و روش متشکل از مدل باس

ABSTRACT

Online reviews provide consumers with rich information that may reduce their uncertainty regarding purchases. As such, these reviews have a significant influence on product sales. In this paper, a novel method that combines the Bass/Norton model and sentiment analysis while using historical sales data and online review data is developed for product sales forecasting. A sentiment analysis method, the Naive Bayes algorithm, is used to extract the sentiment index from the content of each online review and integrate it into the imitation coefficient of the Bass/ Norton model to improve the forecasting accuracy.We collected real-world automotive industry data and related online reviews. The computational results indicate that the combination of the Bass/Norton model and sentiment analysis has higher forecasting accuracy than the standard Bass/Norton model and some other sales forecasting models.

5. Conclusion

In this paper, a forecasting model that combines the Bass/Norton model and sentiment analysis techniques is proposed. In contrast to the extant literature that uses online ratings, this paper extends the Bass model by analyzing sentiments expressed in online reviews. In contrast to the original Bass model, both historical sales and online review data are directly used in the extended model. The NB method is adopted to calculate the sentiment index and conduct polarity classifi- cations for each online review, and the extracted sentiment index is used to expand the imitation coefficient in the Bass model. The same method is used to expand the Norton model. Sentiment information is rarely used to extend the Bass model in existing studies.