Analyzing the Online Reviews to Explore Recent Trends of the U.S. Automotive Industry by Latent Dirichlet Allocation Method

Te Yu Liao, Yu Chih Kao, Ming Shien Cheng, Ping Yu Hsu

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

This study selects the U.S. automotive industry as the research subject to explore the recent trends in automotive development. Since the analysis was based on the content of reviews, a topic model for textual analysis is chosen as the methodology, with Latent Dirichlet Allocation (LDA) being the most representative topic model. A total of 7,492 online reviews of the U.S. automotive industry from the years 2020, 2021, and 2022 were collected. These data were then preprocessed to prepare them for input into the pre-set LDA model. The results of this study are as follows. In the market analysis, driving experience, comfort, interior, gas mileage, and other words for all years correlate with price/performance rankings in the U.S. automotive media. Based on the analysis of the 2020 Truck and SUV, the 2021 and 2022 SUV and Hybrid models, as well as the sales data of Tesla electric vehicles in 2022, a growing trend of Hybrid and pure electric vehicles could observe.

原文???core.languages.en_GB???
主出版物標題Advances in Swarm Intelligence - 15th International Conference on Swarm Intelligence, ICSI 2024, Proceedings
編輯Ying Tan, Yuhui Shi
發行者Springer Science and Business Media Deutschland GmbH
頁面437-448
頁數12
ISBN(列印)9789819771837
DOIs
出版狀態已出版 - 2024
事件15th International Conference on Swarm Intelligence, ICSI 2024 - Xining, China
持續時間: 23 8月 202426 8月 2024

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
14789 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

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???event.eventtypes.event.conference???15th International Conference on Swarm Intelligence, ICSI 2024
國家/地區China
城市Xining
期間23/08/2426/08/24

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