## 3 Results
On average, the precision of the CNN-LSTM model (0.77) was 0.22 higher than that of the sentimental model (0.55). In total, the precision of the CNN-LSTM model was an average of 0.39 higher than that of the classical model. Consequently, according to the above comparison, the precision of the forecasting of car sales movement directions in Taiwan was effectively improved by the use of online sentiment data and the CNN-LSTM model. Figure 6 shows the recall of the classical, sentimental, and the CNN-LSTM models for the six car brands. As mentioned above, recall is a measure of the ability of a model to correctly identify the observations of a specific car sales movement direction within the dataset. As shown in Figure 6, the recalls of the sentimental model for all six brands but TOYOTA were higher than those of the classical model. TOYOTA had a consistent recall of 0.50 in both the classical and sentimental models. On average, the recall of the sentimental model (0.51) was 0.09 higher than that of the classical model (0.42). Furthermore, the recalls of the CNN-LSTM model for all six brands were higher than those of the sentimental model. On average, the recall of the CNN-LSTM model (0.69) was 0.18 higher than that of the sentimental  
Figure 5: The precision of the classical, sentimental, and the CNN-LSTM models for six car brands. of the sentimental model (0.51). In total, the CNN-LSTM model demonstrated an average improvement in recall of 0.27 over that of the classical model. According to the above comparison, the recall of forecasting of car sales movement directions in Taiwan was improved by the use of online sentiment data and the CNN-LSTM model. Finally, the results of F1-scores simultaneously considering precision and recall are reviewed. Figure 7 shows the F1-scores of the classical, sentimental, and the CNN-LSTM models for the six car brands. As mentioned above, the F1-score is more useful than accuracy when the dataset has an uneven class distribution. As shown in Figure 7, the F1-scores of the sentimental model for all six brands were higher than those of the classical model. On average, the F1-scores of the sentimental model (0.46) were 0.11 higher than those of the classical model (0.35). Furthermore, the F1-scores of the CNN-LSTM model for all six brands were higher than those of the sentimental model. On average, the F1-scores of the CNN-LSTM model (0.68) were 0.22 higher than those of the sentimental model (0.46). Overall, the CNN-LSTM model demonstrated an average improvement in F1-score of 0.33 over that of the classical model. Based on the above comparison, the F1-score of forecasting of car sales movement directions was improved by the use of online sentiment data and the CNN-LSTM model. The comparisons of the four indices in the classical, sentimental, and the CNN-LSTM models have shown that the performance of forecasting of car sales movement directions in Taiwan was improved by the use of online sentiment data and the CNN-LSTM model. However, accuracy works best if kinds of false predictions have similar costs. If the costs of different false predictions are very dissimilar, it is better to look at precision or recall. In this research, the costs of classifying direction D as F or U, classifying direction F as D or U, and classifying direction U as D or F were different, so precision and recall were further explored to examine the applicability of the proposed methods. Figure 6: The recall of the classical, sentimental, and the CNN-LSTM models for six car brands. As mentioned above, precision is used to evaluate the ability of a model to correctly predict each kind of car sales movement directions; recall is used to evaluate the ability of a model to correctly identify the observations in each kind of car sales movement directions in the dataset. In practice, car companies arrange their production and sales plans according to the predictions of car sales movement directions, so the correctness of the prediction, i.e., the precision, will have a direct impact on car inventories and sales.