## 1 Introduction  
Forecasting, which can help managers to develop more accurate and meaningful plans have played an important role in reducing business uncertainty for companies [1]. Sales forecasting in particular is the basis of definite and reliable plans for marketing, sales management, production, procurement, and logistics, which further empower companies to provide better services and reap more benefits [2]. A successful sales forecast is an essential key for companies to manage their business successfully. The automobile industry, the leading producer of machines in many countries, is important for worldwide economic development. Furthermore, manufacturing a car requires iron, aluminum, plastic, steel, glass, rubber, copper, and more materials. If an automobile company can accurately predict its car sales, it can arrange effective production plans for its supply chain to prevent shortages and excesses of materials in the inventory process. In addition, when a customer decides to buy a new car, he/she generally hopes to take possession of the vehicle as soon as possible. If an automobile company can accurately predict its sales, it can develop effective sales plans to provide good service to its customers. Therefore, the development of a good car sales forecasting method is important for the automobile industry. Unfortunately, few studies to date have focused on car sales forecasting [2; 3; 4; 5; 6; 7; 8]. [PERSON] and [PERSON] [8] assembled a curve-regression model, a time series decomposition model, and RBF neural networks as a combined forecasting model and used economic data which takes on the obvious time factor and trends in car-making and selling. [PERSON] et al. [3] developed a time series model consisting of additive components: trend, seasonal, calendar, and error components. The model collected the main time series of newly registered automobiles and a secondary time series of exogenous parameters which could influence the trend of the main time series. The trend component was estimated by Multiple Linear Regression and Support Vector Machine (SVM). Yearly, quarterly, and monthly data for newly registered automobiles served as the basis for the tests of the models. The outcomes showed that the quarterly data provided the most accurate results. [PERSON] et al. [4] developed an automobile sales forecasting methodology based on monthly sales volume, coincident indicator, leading indicator, wholesale price index, and income. Then an adaptive network-based fuzzy inference system (ANFIS) was created to obtain the forecast. The automobile forecasting methodology developed by [PERSON] et al. [5] used market-specific exogenous parameters, such as gross domestic product (GDP), stock index, personal income, and unemployment rate, on a yearly, quarterly, or monthly basis as the input variables for time series analysis and classical data mining algorithms. On the Internet, consumers enthusiastically share their opinions and reviews via news, blogs, and social media, also known as electronic word of mouth (eWOM), and increasing numbers of potential buyers habitually consult eWOM before making their purchasing decisions [9; 10; 11; 12; 13; 14]. Since eWOM can be positive or negative statements about a product or company [15; 16; 17], researchers have proposed sentiment analysis methods for automatically distinguishing three types of eWOM: positive, negative, and neutral [18]. To simultaneously apply historical sales data and eWOM to car sales forecasting, [PERSON] et al. [2] used a sentiment analysis method, the Naive Bayes (NB) algorithm, to extract the sentiment index from each online review, and then integrated the sentiment index into the imitation coefficient of the Bass/Norton model to improve the forecasting accuracy. Although very little effort has been expended to examine car sales forecasting, several points can still be raised by referring to forecasting studies of both car sales and sales of other products to facilitate the improvement of car sales forecasting methods.