## 3 Results
Since the planned production quantity will be higher or lower than the market demand, car companies may either lose sales and market share or hold excess inventory. As shown in Figure 10, the Precision(F) values of the sentimental model for all six brands except BENZ were equal to or higher than those of the classical model. The Precision(F) increased by 0.15 on average, from 0.07 for the classical model to 0.22 for the sentimental model. Comparatively, the Precision(F) values of the CNN-LSTM model for all six brands were higher than those of the sentimental model, as was the improvement of 0.57, from the average of 0.22 of the sentimental model to the average of 0.79 of the CNN-LSTM model. Although both the online sentiment data and the CNN-LSTM model contributed to the improvement of Precision(F) values, the effect of the CNN-LSTM method was particularly significant. As for BENZ, although the Precision(F) dropped from 0.43 in the classical model to 0.00 in the sentimental model, the integral effect of online sentiment data and the CNN-LSTM method enhanced the Precision(F) from 0.43 for the classical model to 0.67 for the CNN-LSTM model. From the above analysis, it can be found that the precisions of the three directions, Precision(D), Precision(F), and Precision(U), all improved gradually from the classical model to sentimental and the CNN-LSTM model. Therefore, the results clearly indicated that the online sentiment data and CNN-LSTM method improved the precision of the forecasting of directions D, F, and U in car sales in Taiwan. However, the extent of the effects of online sentiment data and CNN-LSTM method on Precision(D), Precision(F), and Precision(U) were different. The contribution to Precision(U) of the online sentiment data was 0.06, while that from the CNN-LSTM method was 0.10. That is, Precision(U) gained only a little improvement from the online sentiment data and  
Figure 9: The precision of direction U of the classical, sentimental, and the CNN-LSTM models for six car brands. CNN-LSTM method. For Precision(D), in contrast, the contributions of the online sentiment data and the CNN-LSTM method were 0.39 and 0.14, respectively. Thus, Precision(D) gained a lager improvement from the online sentiment data than from the CNN-LSTM method. Furthermore, the Precision(F) gained only 0.15 in improvement from the online sentiment data but 0.57 from the CNN-LSTM method. Obviously, most of the improvement of Precision(F) resulted from the CNN-LSTM method. A further discussion of how the online sentiment data and CNN-LSTM method impacted Precision(D), Precision(F), and Precision(U) is provided as follows. As mentioned in Section 2.1, this research collected and analyzed three types of online sentiment volumes, i.e., positive (P), negative (N), and total (T), to predict car sales movement direction for six car brands. Theoretically, positive sentiment is consumer responses indicating satisfaction; negative sentiment is consumer responses suggesting dissatisfaction. A more positive sentiment volume in the previous months indicates an increased likelihood of a sales movement direction of U in the next month. Conversely, a more negative sentiment volume in the previous months increases the likelihood of a sales movement direction of D in the next month. In practice, the originators and motives of eWOMs and online sentiment will influence consumer intentions and decisions, which in turn will impact the overall performance of sales movement direction forecasting. The main originators of eWOMs and online sentiment include the customers and the industry. The online sentiments of customers who have real praise (positive eWOMs) or complaints (negative eWOMs) may directly affect the sales movement directions of the subsequent periods. On the other hand, the online sentiments from the profession include eWOMs written by the companies themselves, the company's partners, or the company's competitors.