Online reviews contain a great deal of information about consumers' purchasing preferences, which seriously affects potential consumers' purchasing decisions. Using the online review data to help customers make purchasing decisions has become a concern of customers, which has theoretical and practical application value. Therefore, a product selection model is presented based on sentiment analysis combined with an intuitionistic fuzzy TODIM method. Firstly, the product features are extracted by the Apriori algorithm based on online reviews. The sentiment orientation and intensity of the sentiment words for the product features are identified by the lexicon-based sentiment analysis approach. Next, the sentiment orientation of the product features is represented by an intuitionistic fuzzy value. Then the intuitionistic fuzzy TODIM method is used to determine the ranking results of the alternative products. Finally, the case study of mobile phone selection is given to illustrate the proposed approach. The results show that the proposed method considers the online reviews’ sentiment orientation and intensity and the consumers’ gain and loss in the purchasing product process and is more reasonable than the previous research.

Classical time series model can efficiently handle many forecasting problems, but these models can not solves the forecasting problem in which values of the time series are represented by language values or fuzzy sets. Song and Chissom and many other scholars put forward many models, and these models can only forecast research about historical data. This paper presents a new fuzzy time series forecasting model which can predict the data of unknown years.

Since Song and Chissom proposed fuzzy time series forecasting theory, already exceed in the 20 years. Scholars have proposed many fuzzy time series forecasting models, the prediction accuracy of historical simulation data continues to improve. Unfortunately has not hitherto given for fuzzy time series forecasting model about the data of unknown years. This paper presents an improved forecasting model of fuzzy time series. It may predict the historical simulation data, but also may predict the unknown year data.

No abstract

No abstract