隨著近年網路使用者的大量增加，許多人在做出相對高價之消費決策時經常會選擇網路討論與瀏覽推文的方式做為消費的參考依據。對廠商而言，運用網路做行銷已經是主要的行銷通路之一，網路的宣傳與網民意見可以視為左右名聲的重要指標。尤其是高價之消費品如汽車，因其品牌競爭與車型種類的繁多，在網路上討論以協助購買決策已是常見的模式。特別是國內網路文章中關於汽車的討論一直是相當活躍的群體。
本研究基於上述理由，利用汽車品牌於國內網路聲量資料，進行預測並判斷銷售量趨勢，並希望運用這項預測資訊，可以提供生產與進口的廠商作為行銷與生產管理方面之參考，進而促進汽車產業之發展。
另外，本研究所採用之網路聲量資料來源，係國內知名之網路聲量分析公司，其提供之資訊在國內受到廣泛使用。本研究除用該公司的汽車品牌網路總聲量資料，將採用該公司標註之正聲量、負聲量做為情緒資料變數，以提高模型判斷準確率。而預測目標將使用交通部公路總局網站所公開之每月汽車掛牌數，並轉換成趨勢上升、保持或下跌標籤。
本研究所採用的網路聲量情緒資料具有時間序列資料之特性，所以在預測模型方面將使用一維卷積神經網絡(1 Dimension Convolution Neural Networks, 1D CNN)，以及長短期記憶網絡(Long Short Term Memory Network, LSTM)作為預測模型的主軸，並加入CNN與LSTM的複合模型，對架構進行測試與研究，活用這類深度學習模型之特性並提供實務上的案例，建立針對個案資料最適配之模型架構。同時，本研究屬於現實的小樣本時間序列資料，在方法與研究上會針對其特殊的資料特性選配，期待會對本研究之模型預測有所幫助。

Applying network opinions as a reference to shopping becomes popular in recent years. Many social networks medias such as discussion forums and blogs growth rapidly. More and more businesses have utilized social networks as their marketing strategies. The market for automobile sales is one of the areas. People will go to various social media to search for related information before making the final decision. Therefore, this research tends to apply network opinions from various social media to predict the trends of automobile sales.
This research will use the positive/negative emotion and total internet volume for six brands of automobiles and their automobiles sales data as the dataset. The time series of internet volume data are collected and analyzed every day and car sales data are recorded every month.
The proposed approach including several steps. First, convert car sales data into a trend up, hold, or down labels. Second, use genetic algorithms to reduce the number of variables to facilitate model training. Then a hybrid “One Dimension Convolution Neural Networks (1D CNN)” with “Long Short Term Memory Network (LSTM)” will be used to predict the sales trends. Finally, compare the prediction results with LSTM and 1D CNN models. The predicting results can be used as a reference for the marking and production departments of local automobile companies.

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