## 1 Introduction
First, historical sales data are the major predictor variable used for sales forecasting. Several other predictor variables, such as product prices, advertising campaigns, holidays [19], and economic indicators [4], are also frequently used for sales forecasting. Recently, with the popularity of social media, studies have begun using online reviews [2], online promotional strategies [20], and sentiment analysis [2; 20; 21] as the predictor variables to improve the performance of sales forecasting. Second, several linear and nonlinear models, such as the Delphi technique, exponential smoothing, regression analysis, autoregressive integrated moving average (ARIMA), bass diffusion model, and multinomial logistic regression (MLR), are classical methods employed for sales forecasting and other predictions [4; 22; 23]. However, with the development of deep learning techniques, such as the Convolution Neural Networks (CNNs) and Long Short-Term Memory (LSTM), deep learning techniques have been recently applied to sales forecasting to improve the prediction performance [4; 19; 20; 24; 25]. The CNN is usually applied to image data for solving classification problems [26], while LSTM is used to analyze time series data for solving classification, processing, and forecasting problems [27]. Third, the response variable of sales forecasting can be either the sales volume (or amount) or the sales movement direction [18; 28]. Sales volume forecasting is a continuous value prediction of sales volume. In contrast, the sales movement direction transforms the sales volume into directional changes in sales, such as Up, Flat, and Down. Thus, sales movement direction forecasting is a classification problem of sales forecasting. In Taiwan's automobile industry, the sales volume of passenger cars in 2019 was 383,987. As consumer preferences changed, the sales of imported cars in Taiwan increased year by year. The 2019 sales volume of imported cars was 200,548, i.e., about 52% of the market share of passenger cars. The total sales volume of the top six leading imported car brands, namely, BMW, Lexus, Mazda, Mercedes-Benz (Benz), Toyota, and Volkswagen (VW), was 146,231, around 73% of the market share of imported cars [29]. Thus, accurately predicting car sales, especially for the top six leading imported car brands, could contribute to the development of Taiwan's automobile industry. Consequently, this research aimed to improve the sales forecasting for Taiwan's car industry and used the top six leading imported car brands as the experiment cases. As mentioned above, the response variable of car sales forecasting can be either the sales volume (or amount) or the sales movement direction. [PERSON] and [PERSON] [7] argued that correct forecasts of car sales movement directions can still provide useful information even with large errors in the forecast car sales volumes. This is particularly important when predicting a turning point, which is a special case of directional accuracy and represents a change in the car sales movement direction. Therefore, this research selected the car sales movement direction as the response variable for the sales forecasting of Taiwan's six leading imported car brands. In addition, because a car is a durable consumer good, potential buyers will spend more time on eWOM to aid in decision-making on the purchase. To improve the performance of car sales forecasting, in addition to historical sales data, multi-channel online sentiment data were also used as the predictor variables for car sales forecasting. Instead of regarding the sentiment data as a coefficient of the Bass/Norton model in [PERSON] et al. 's study [2], this research prepared and analyzed a series of daily multi-channel online sentiment data of a car brand in the form of images. Therefore, to consider the image characters of online sentiment data and the time series characteristics of historical car sales data, a CNN-LSTM model integrating the CNN and LSTM networks was used to build a car sales prediction model with improved prediction performance.