## 3 Results
Thus, in the application of car sales movement direction forecasting, precision is more important than recall. The following section discusses the precision of the three models for the six car brands in greater detail. Figure 8 illustrates the precision for direction D, Precision(D), of the classical, sentimental, and the CNN-LSTM models for the six car brands. A low Precision(D) means that most of the predictions of direction D are incorrect and should be F or U, so the planned production quantity will not meet the market demand. Improving Precision(D) can prevent losses of sales and market share due to insufficient production. As shown in Figure 8, the Precision(D) values of the sentimental model for all six brands were equal to or higher than those of the classical model. On average, the Precision(D) values increased by 0.39, from 0.34 in the classical model to 0.73 in the sentimental model. Furthermore, the Precision(D) of the CNN-LSTM model for all six brands except VW were equal to or higher than those of the sentimental model. The Precision(D) of the sentimental model was enhanced by 0.11 in the CNN-LSTM model, from 0.73 to 0.84 on average. Both the online sentiment data and the CNN-LSTM method were conducive to the improvement of Precision(D) for forecasting car sales movement directions in Taiwan. The integral effect of improving Precision(D) by using online sentiment data and the CNN-LSTM method was 0.50 (from the average of 0.34 in the classical model to the average of 0.84 in the CNN-LSTM model). Obviously, the effect of the online sentimental data (0.39) in the sentimental model on the improvement of Precision(D) was particularly significant. As for VW, although the Precision(D) dropped from 1.00 for the sentimental model to 0.75 for the CNN-LSTM model, the integral effects of  
Figure 7: The F1-score of the classical, sentimental, and the CNN-LSTM models for six car brands. For the precision of direction U, Figure 9 shows the Precision(U) of the three models for the six car brands. A low Precision(U) indicates that most of the predictions of direction U are incorrect and should be F or D, so car companies will plan a production quantity that exceeds market demand. Improving Precision(U) can avoid a surplus of cars. As shown in Figure 9, the Precision(U) values of the sentimental model for all six brands except TOYOTA were equal to or higher than those of the classical model. On average, the Precision(U) increased by 0.06, from 0.57 in the classical model to 0.63 in the sentimental model. Furthermore, the Precision(U) values of the CNN-LSTM model for all six brands except Lexus were equal to or higher than those of the sentimental model. The Precision(U) demonstrated an average improvement of 0.10, from 0.63 for the sentimental model to 0.73 for the CNN-LSTM model. Thus, it was found that the Precision(U) of forecasting of car sales movement directions in Taiwan was also somewhat improved by the use of online sentiment data and the CNN-LSTM method. The integral effect of online sentiment data and the CNN-LSTM method on the Precision(U) was an increase of 0.16 (from the average of 0.57 in the classical model to the average of 0.73 in the CNN-LSTM model). The effect of the CNN-LSTM method (0.16) was a little larger than that of the online sentiment data (0.06). However, TOYOTA and Lexus were not thus affected. The use of online sentiment data may have interfered with the Precision(U) improvement for TOYOTA, for it dropped from 1.00 in the classical model to 0.57 in the sentimental model. On the other hand, the CNN-LSTM appeared unable to raise the Precision(U) for Lexus, which dropped from 1.00 in the sentimental model to 0.70 in the CNN-LSTM model. Figure 8: The precision of direction D of the classical, sentimental, and the CNN-LSTM models for six car brands. Finally, the precision of direction F, Precision(F), for the three models and six car brands are shown in Figure 10. A low Precision(F) value indicates that most of the predictions of direction F are incorrect and should be D or U.