## 4 Conclusions  
With the explosive growth of social media and emerging forecasting methods, the efforts to improve the performance of car sales forecasting should consider the adoption of eWOM, online sentiment data, and some deep learning techniques. The purpose of this research was to improve the overall performance of forecasting of car sales movement directions in Taiwan by using online sentiment data and the CNN-LSTM method. This research selected the car sales movement direction as the predicted object, and it was defined by the sales growth rate of the next month and the predefined threshold. Therefore, in practical application, if a threshold is set at 10% and the car sales movement direction predicted by the CNN-LSTM model is up (U) or down (D), the car company can adjust the production and sales of the next month upward or downward by 10%. To verify the effects of online sentiment data and the CNN-LSTM method on the forecasting of car sales movement directions in Taiwan, three forecasting models, namely, the classical model, the sentimental model, and the CNN-LSTM model, were constructed and compared. The results showed that of the use of both online sentiment data and the CNN-LSTM method led to significant improvements over the classical model in accuracy (27.78%, from 41.67% to 69.45%), precision (0.39, from 0.38 to 0.77), recall (0.27, from 0.42 to 0.69), and F1-score (0.33, from 0.35 to 0.68). It is found that the overall performance of forecasting of car sales movement directions in Taiwan can be effectively improved by the use of online sentiment data and the CNN-LSTM model. Furthermore, because car companies use prediction of car sales movement directions to arrange their production and sales plans, the degree of precision will impact directly both car inventories and sales, so this degree needs to be explored in more detail. The results showed that model with the online sentiment data and the CNN-LSTM method demonstrated on the improvements in Precision(U), Precision(D), and Precision(F) of 0.16 (from 0.57 to 0.73), 0.50 (from 0.34 to 0.84), and 0.72 (from 0.07 to 0.79) respectively. In practice, if direction U is wrongly predicted as direction F or D, the planned production quantity will fall behind the market demand and may incur some opportunity cost, but the impact is not significant. Since loyal customers may just delay their orders, there is little impact on sales volume or market share loss. In contrast, if direction D is wrongly predicted as direction F or U, the acting cost will be high. Since the planned production quantity will exceed the market demand, automobile manufacturers may hold excess inventory, in which can lead to a backlog of capital or the need to sell at a reduced price. Therefore, improvements of Precision(U), Precision(F) and Precision(D) will be of great benefit to car companies in developing effective production and sales plans. Besides, many deep learning models have been developed. Some models are available for this research. For example, the LSTM-vanilla model which only adds a peephole connection to the classic LSTM is similar to the LSTM used in the CNN-LSTM model for this research. In the future, the LSTM-vanilla model may be used to improve the prediction performance and compare with the CNN-LSTM model proposed in this research. Conceptualization, C.O.-Y. and Y.-C.J. ; Methodology, C.O.-Y., S.-C.C. and Y.-C.J. ; Software, S.-C.C. ; Validation, Y.-C.J. ; Formal analysis, C.O.-Y., S.-C.C. and Y.-C.J. ; Investigation, S.-C.C. ; Resources, C.O.-Y. ; Data curation, S.-C.C. ; Writing--original draft preparation, S.-C.C. ; Writing--review and editing, C.O.-Y. and Y.-C.J. ; Visualization, S.-C.C. ; Supervision, C.O.-Y. and Y.-C.J. ; Project administration, C.O.-Y. ; Funding acquisition, C.O.-Y. All authors have read and agreed to the published version of the manuscript. This study was funded by the Ministry of Science and Technology of the Republic of China, Taiwan, under contract no. MOST 109-2221-E-011-102. Not applicable.