Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model
The automotive industry is the leading producer of machines in Taiwan and worldwide. Developing effective methods for forecasting car sales can allow car companies to arrange their production and sales plans. Capitalizing on the growth of social media and deep learning algorithms, this research aime...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/3/1550 |
| _version_ | 1797489059005726720 |
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| author | Chao Ou-Yang Shih-Chung Chou Yeh-Chun Juan |
| author_facet | Chao Ou-Yang Shih-Chung Chou Yeh-Chun Juan |
| author_sort | Chao Ou-Yang |
| collection | DOAJ |
| description | The automotive industry is the leading producer of machines in Taiwan and worldwide. Developing effective methods for forecasting car sales can allow car companies to arrange their production and sales plans. Capitalizing on the growth of social media and deep learning algorithms, this research aimed to improve the overall performance of the forecasting of Taiwan car sales movement direction forecasting by using online sentiment data and CNN-LSTM method. First, the historical sales volumes and multi-channel online sentiment data for six car brands in Taiwan were collected and preprocessed for labeling of car sales movement direction. Then, three models, namely, the classical, sentimental, and CNN-LSTM models, were constructed and trained/fitted for forecasting car sales movement directions in Taiwan. Finally, the performance of the three prediction models were compared to verify the effects of online sentiment data and the CNN-LSTM model on forecasting performance. The results showed that four forecasting performance indices, i.e., accuracy, precision, recall and F1-score, improved by 27.78% (from 41.67% to 69.45%), 0.39 (from 0.38 to 0.77), 0.27 (from 0.42 to 0.69) and 0.33 (from 0.35 to 0.68), respectively. Therefore, the online sentiment data and CNN-LSTM method can indeed improve the overall performance of car sales movement direction in Taiwan. |
| first_indexed | 2024-03-10T00:11:06Z |
| format | Article |
| id | doaj.art-5eae249ceba24b2dbe0e2620f8e87c77 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:11:06Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5eae249ceba24b2dbe0e2620f8e87c772023-11-23T15:59:27ZengMDPI AGApplied Sciences2076-34172022-01-01123155010.3390/app12031550Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM ModelChao Ou-Yang0Shih-Chung Chou1Yeh-Chun Juan2Department of Industrial Management, National Taiwan University of Science and Technology (Taiwan Tech), Taipei 106, TaiwanDepartment of Industrial Management, National Taiwan University of Science and Technology (Taiwan Tech), Taipei 106, TaiwanDepartment of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243, TaiwanThe automotive industry is the leading producer of machines in Taiwan and worldwide. Developing effective methods for forecasting car sales can allow car companies to arrange their production and sales plans. Capitalizing on the growth of social media and deep learning algorithms, this research aimed to improve the overall performance of the forecasting of Taiwan car sales movement direction forecasting by using online sentiment data and CNN-LSTM method. First, the historical sales volumes and multi-channel online sentiment data for six car brands in Taiwan were collected and preprocessed for labeling of car sales movement direction. Then, three models, namely, the classical, sentimental, and CNN-LSTM models, were constructed and trained/fitted for forecasting car sales movement directions in Taiwan. Finally, the performance of the three prediction models were compared to verify the effects of online sentiment data and the CNN-LSTM model on forecasting performance. The results showed that four forecasting performance indices, i.e., accuracy, precision, recall and F1-score, improved by 27.78% (from 41.67% to 69.45%), 0.39 (from 0.38 to 0.77), 0.27 (from 0.42 to 0.69) and 0.33 (from 0.35 to 0.68), respectively. Therefore, the online sentiment data and CNN-LSTM method can indeed improve the overall performance of car sales movement direction in Taiwan.https://www.mdpi.com/2076-3417/12/3/1550automotive industrysales forecastingonline sentiment analysiselectronic word of mouth (eWOM)Convolution Neural Networks (CNN)Long Short-Term Memory (LSTM) |
| spellingShingle | Chao Ou-Yang Shih-Chung Chou Yeh-Chun Juan Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model Applied Sciences automotive industry sales forecasting online sentiment analysis electronic word of mouth (eWOM) Convolution Neural Networks (CNN) Long Short-Term Memory (LSTM) |
| title | Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model |
| title_full | Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model |
| title_fullStr | Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model |
| title_full_unstemmed | Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model |
| title_short | Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model |
| title_sort | improving the forecasting performance of taiwan car sales movement direction using online sentiment data and cnn lstm model |
| topic | automotive industry sales forecasting online sentiment analysis electronic word of mouth (eWOM) Convolution Neural Networks (CNN) Long Short-Term Memory (LSTM) |
| url | https://www.mdpi.com/2076-3417/12/3/1550 |
| work_keys_str_mv | AT chaoouyang improvingtheforecastingperformanceoftaiwancarsalesmovementdirectionusingonlinesentimentdataandcnnlstmmodel AT shihchungchou improvingtheforecastingperformanceoftaiwancarsalesmovementdirectionusingonlinesentimentdataandcnnlstmmodel AT yehchunjuan improvingtheforecastingperformanceoftaiwancarsalesmovementdirectionusingonlinesentimentdataandcnnlstmmodel |