現在的車輛已開始將自動駕駛系統（Automated Driving Systems, ADS）的相關功能加進汽車安全輔助系統功能中，為車輛駕駛人及其他用路人創造出更安全且便利的道路生活環境。自動駕駛系統以機器分析判斷，可協助車輛避免危害，以及處理駕駛人所無法顧及突發狀況，甚至自動駕駛系統能直接介入人員駕駛中的車輛，進行控制，來防止意外發生。現階段自動駕駛系統仍處於發展萌芽期，仍有賴更多的創新研發技術來提升自動駕駛車輛的完整性，最終勢將發展成為無需人類駕駛之運載工具，所以消費者對自動駕駛系統車輛之信賴程度，將影響未來自駕車之研究與發展。
為廣範擷取感受自動駕駛車輛的相關數據，運用大數據技術分析的方式從母體進行分析，對於次世代自動駕駛系統之開發，至關重要，但少有學者從事相關研究。為跨越此研究缺口，本研究擬導入文字探勘，由社群網路挖掘特定議題或產品、功能或服務之使用者意見，挖掘大眾對目標議題之認知及經驗，然後將這些資訊彙整後摘整，轉換成結構化資訊，進行分析。因此，本研究擬探勘社群網站，搭配與自動駕駛系統技術有關之關鍵字，進行資料採集、擷取特徵，並利用「隱含Dirichlet配置模型（Latent Dirichlet Allocation, LDA）」建立文件主題模型，將主題歸入整合科技接受模型理論之構面後，作為理論架構，並發展對應之假設。最後導入偏最小平方法結構方程模型式（Partial Least Squares Structural Equation Modelling, PLS-SEM）驗證之。本研究將以探勘臺灣社群媒體網站之結果，驗證分析架構之可行性，所得消費者選擇使用自動駕駛系統車輛的行為意圖，包括希望帶來更好的正面效果，使車輛更容易操控，以及發展對內嵌自動駕駛系統之車輛有利的駕駛環境，將正向影響消費者的實際使用行為。研究結論可作為發展內嵌自動駕駛系統車輛設計開發及訂定行銷策略之參據，驗證完善之分析架構，也可以作為擷取影響接受其他突破性或漸進式創新關鍵要素之用。

Automated Driving Systems (ADS) are now being added to vehicle safety assistance systems to create a safer and more convenient road environment for vehicle drivers and other road users. ADS uses machine analysis and judgment to help vehicles avoid hazards and deal with unexpected situations that drivers cannot take into account. Even ADS can directly intervene in the vehicle being driven by a person to control it to prevent accidents. Now the ADS is still in the budding stage of development. It still depends on more innovative research and development technologies to enhance the integrity of self-driving vehicles. It will develop into a vehicle that does not need to be driven by human eventually. Therefore, the level of consumer trust in self-driving vehicles will influence the future research and development of self-driving vehicles.
In order to obtain a wide range of data related to the feeling of self-driving vehicles. The use of big data technology to analyze the data from the parent is very important for the development of next-generation automated driving systems, but few scholars have conducted related research. In order to bridge this research gap, this research intends to use textual prospecting techniques to mine user opinions on specific issues or products, features, or services in social networks. In this way, we can obtain the public's knowledge and experience of the target issue. This information is then aggregated and extracted into structured information, and finally analyzed. Therefore, this research intends to conduct social media mining in the website with keywords related to ADS technology to collect data, extract features, and build a document topic model using Latent Dirichlet Allocation (LDA). The topics are then grouped into Unified Theory of Acceptance and Use of Technology (UTAUT) as a theoretical framework, and corresponding assumptions are developed. Finally, Partial Least Squares Structural Equation Modelling (PLS-SEM) is used for validation. In this research, the results of mining Taiwanese social media sites are used to verify the feasibility of the framework. The behavioral intent of consumers choosing to use ADS vehicles was obtained. These include the desire for better positive outcomes, easier handling of the vehicle, and the development of a driving environment conducive to ADS vehicles, which will positively influence the actual usage behavior of consumers. The findings of the research can be used as a reference for the development of embedded ADS vehicle design and marketing strategies. The validated analytical framework can also be used to influence the acceptance of other key elements of radical or incremental innovation.

Aarts, H., Verplanken, B., & Van Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology, 28(15), 1355-1374.
Acheampong, R. A., & Cugurullo, F. (2019). Capturing the behavioural determinants behind the adoption of autonomous vehicles: Conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars. Journal of the Academy of Marketing Science, 62, 349-375.
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16, 227-247.
Akilan, A. (2015). Text mining: Challenges and future directions. Paper presented at the 2015 2nd International Conference on Electronics and Communication Systems (ICECS).
Araque, O., Zhu, G., & Iglesias, C. A. (2019). A semantic similarity-based perspective of affect lexicons for sentiment analysis. Knowledge-Based Systems, 165, 346-359.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
Bakalos, N., Papadakis, N., & Litke, A. (2020). Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data. Logistics, 4(2), 12.
Berry, M., W. (2004). Automatic discovery of similar words. Survey of Text Mining: Clustering, Classification and Retrieval, 24-43.
Bimbraw, K. (2015). Autonomous cars: Past, present and future a review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology. Paper presented at the 2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO).
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. the Journal of machine Learning research, 3, 993-1022.
Brookhuis, K. A., De Waard, D., & Janssen, W. H. (2001). Behavioural impacts of advanced driver assistance systems–an overview. European Journal of Transport and Infrastructure Research, 1(3), 245-253.
Buckley, L., Kaye, S.-A., & Pradhan, A. K. (2018). Psychosocial factors associated with intended use of automated vehicles: A simulated driving study. Accident Analysis & Prevention, 115, 202-208.
Burke, R. R. (2002). Technology and the customer interface: What consumers want in the physical and virtual store. Journal of the Academy of Marketing Science, 30(4), 411-432.
Camacho, D., Luzón, M. V., & Cambria, E. (2020). New research methods & algorithms in social network analysis. Future Generation Computer Systems, 114, 290-293.
Chakraborty, G., & Krishna, M. (2014). Analysis of unstructured data: Applications of text analytics and sentiment mining. Paper presented at the SAS Global Forum.
Chen, M.-Y., Liao, C.-H., & Hsieh, R.-P. (2019). Modeling public mood and emotion: Stock market trend prediction with anticipatory computing approach. Computers in Human Behavior, 101, 402-408.
Chen, Y. N. (2020). Derivations of factors influencing the adoption of smart bracelets by social media mining and PLS-SEM. (Unpublished master’s dissertation). National Taiwan Normal University, Taipei.
Chong, A. Y. L. (2013). Mobile commerce usage activities: The roles of demographic and motivation variables. Technological Forecasting and Social Change, 80(7), 1350-1359.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2th ed.). New York, United States: Lawrence Earlbaum Associates.
Coltman, T., Devinney, T. M., Midgley, D. F., & Venaik, S. (2008). Formative versus reflective measurement models: Two applications of formative measurement. Journal of Business Research, 61(12), 1250-1262.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology,
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
de Almeida Correia, G. H., & van Arem, B. (2016). Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility. Transportation Research Part B: Methodological, 87, 64-88.
Dijkstra, T. K. (2014). PLS'Janus face–response to professor Rigdon's ‘rethinking partial least squares modeling: In praise of simple methods’. Long Range Planning, 47(3), 146-153.
Duan, H. K. (2020). New York City Street Cleanliness: Applying Text Mining Techniques to Social Media Information. Available at SSRN 3706506.
Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2013). Online drivers of consumer purchase of website airline tickets. Journal of Air Transport Management, 32, 58-64.
Finner, H., & Gontscharuk, V. (2018). Two-sample Kolmogorov–Smirnov-type tests revisited: Old and new tests in terms of local levels. The Annals of Statistics, 46(6A), 3014-3037.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Gayko, J. E. (2012). Lane departure and lane keeping. Handbook of Intelligent Vehicles, 1, 689-708.
Gietelink, O., Ploeg, J., De Schutter, B., & Verhaegen, M. (2006). Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations. Vehicle System Dynamics, 44(7), 569-590.
Giovanis, A. N., Tomaras, P., & Zondiros, D. (2013). Suppliers logistics service quality performance and its effect on retailers’ behavioral intentions. Procedia-Social and Behavioral Sciences, 73, 302-309.
Guliashki, V. (2018). An Algorithm for Speed Optimization in Autonomous Vehicles. Paper presented at the 7th Annual International Conference Computer Science and Communication Engineering & Information Systems and Security, Pristina, Kosovo.
Hair, J. F., Anderson, R., Tatham, R., & Black, W. (1998). Factor analysis. Multivariate data analysis. NJ: Prentice-Hall, 3, 98-99.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616-632.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110.
Haus, S. H., Sherony, R., & Gabler, H. C. (2019). Estimated benefit of automated emergency braking systems for vehicle–pedestrian crashes in the United States. Traffic Injury Prevention, 20(sup1), 171-176.
He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472.
Henseler, J. (2017). Partial least squares path modeling. In W. Leeflang, Bijmolt & Pauwels (Ed.), Advanced Methods for Modeling Markets (pp. 361-381). Cham, Switzerland: Springer.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2-20.
Herrenkind, B., Nastjuk, I., Brendel, A. B., Trang, S., & Kolbe, L. M. (2019). Young people’s travel behavior–Using the life-oriented approach to understand the acceptance of autonomous driving. Transportation Research Part D: Transport and Environment, 74, 214-233.
Hojjati-Emami, K., Dhillon, B., & Jenab, K. (2012). Reliability prediction for the vehicles equipped with advanced driver assistance systems (ADAS) and passive safety systems (PSS). International Journal of Industrial Engineering Computations, 3(5), 731-742.
Hung, C. Y. (2012). An Approach to eBook Topics Trend Discovery Based on LDA and Usage Log. (Unpublished master’s dissertation), Kaohsiung.
Hung, S.-Y., Ku, Y.-C., & Chien, J.-C. (2012). Understanding physicians’ acceptance of the Medline system for practicing evidence-based medicine: A decomposed TPB model. International Journal of Medical Informatics, 81(2), 130-142.
Hung, W. (2018). Key Factors Contributing to Ride Hailing Behavior Intention of Robo-Taxi. (Unpublished master`s dissertation). National Central University, Taoyuan, Taiwan.
Ibrahim, N. F., & Wang, X. (2019). Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media. Computers in Human Behavior, 96, 32-45.
Jeng, D. J.-F., & Tzeng, G.-H. (2012). Social influence on the use of clinical decision support systems: Revisiting the unified theory of acceptance and use of technology by the fuzzy DEMATEL technique. Computers & Industrial Engineering, 62(3), 819-828.
Jeong, B., Yoon, J., & Lee, J.-M. (2019). Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management, 48, 280-290.
Ji, J. (2013). A study of external factors affecting college students’ adoption to use mobile phone in the perspective of TAM. (Unpublished master’s dissertation). Nanjing University, Nanjing.
Kanapala, A., Pal, S., & Pamula, R. (2019). Text summarization from legal documents: A survey. Artificial Intelligence Review, 51(3), 371-402.
Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 781-804.
Kaye, S.-A., Lewis, I., Buckley, L., & Rakotonirainy, A. (2019). Examining Queensland drivers’a priori acceptance of conditional and full automated vehicles. Paper presented at the 2019 Australasian Road Safety Conference.
Khatoon, M., Banu, W. A., Zohra, A. A., & Chinthamani, S. (2019). Sentiment Analysis on Tweets. Software Engineering, 731, 717-724.
Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404-416.
Kim, S. G., & Kang, J. (2018). Analyzing the discriminative attributes of products using text mining focused on cosmetic reviews. Information Processing & Management, 54(6), 938-957.
Kovalev, M. S., & Utkin, L. V. (2020). A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov–Smirnov bounds. Neural Networks, 132, 1-18.
Kumar, A., & Patra, R. (2018). Driver drowsiness monitoring system using visual behaviour and machine learning. Paper presented at the 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia.
Kuo, Y. Y. (2013). A Research on The Influence on Customers' Use Intention of Electronic Vehicles with The Technology Acceptance Model TAM. (Unpublished master`s dissertation). National Cheng Kung University, Tainan, Taiwan.
Lee, C.-C., Lin, S.-P., Yang, S.-L., Tsou, M.-Y., & Chang, K.-Y. (2013). Evaluating the influence of perceived organizational learning capability on user acceptance of information technology among operating room nurse staff. Acta Anaesthesiologica Taiwanica, 51(1), 22-27.
Madigan, R., Louw, T., Wilbrink, M., Schieben, A., & Merat, N. (2017). What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transportation Research Part F: Traffic Psychology and Behaviour, 50, 55-64.
Marcoulides, G. A. (1998). The partial least squares approach for structural equation modeling. Modern Methods for Business Research, 295(2), 303-344.
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67-72.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
Navathe, S. B., & Ramez, E. (2000). Data warehousing and data mining. Fundamentals of Database Systems, 841-872.
Nenkova, A., & McKeown, K. (2012). A survey of text summarization techniques. Mining Text Data, 43-76.
Nolasco, D., & Oliveira, J. (2019). Subevents detection through topic modeling in social media posts. Future Generation Computer Systems, 93, 290-303.
Nordhoff, S., Louw, T., Innamaa, S., Lehtonen, E., Beuster, A., Torrao, G., . . . Happee, R. (2020). Using the UTAUT2 model to explain public acceptance of conditionally automated (L3) cars: A questionnaire study among 9,118 car drivers from eight European countries. Transportation Research Part F: Traffic Psychology and Behaviour, 74, 280-297.
Nunnally, J. C. (1978). Psychometric Theory 2nd ed. In. New York, United States: Mcgraw hill book company.
Ozbay, F. A., & Alatas, B. (2020). Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Statistical Mechanics and its Applications, 540, 123174.
Park, S. B., Kim, J., Lee, Y. K., & Ok, C. M. (2020). Visualizing theme park visitors’ emotions using social media analytics and geospatial analytics. Tourism Management, 80, 104127.
Rambaccussing, D., & Kwiatkowski, A. (2020). Forecasting with news sentiment: Evidence with UK newspapers. International Journal of Forecasting, 36(4), 1501-1516.
Razali, N. M., & Wah, Y. B. (2011). Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21-33.
Richter, N. F., Sinkovics, R. R., Ringle, C. M., & Schlägel, C. (2016). A critical look at the use of SEM in international business research. International Marketing Review, 33(3), 376-404.
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor's comments: A critical look at the use of PLS-SEM in" MIS Quarterly". MIS Quarterly, 36(1), 3-14.
Rogers, E. M. (2010). Diffusion of innovations. New York, United States: Simon and Schuster.
SAE On-Road Automated Vehicle Standards Committee. (2018). Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. In Surface Vehicle Information Report J3016.
Salloum, S. A., Al-Emran, M., & Shaalan, K. (2016). A survey of lexical functional grammar in the Arabic context. International Journal of Computing and Network Technology, 4(3), 141-146.
Sanchez-Franco, M. J., Cepeda-Carrion, G., & Roldán, J. L. (2019). Understanding relationship quality in hospitality services: A study based on text analytics and partial least squares. Internet Research, 29(3), 478-503.
Sasikala, G., & Kumar, V. R. (2019). Development of advanced driver assistance system using intelligent surveillance. Paper presented at the International Conference on Computer Networks and Communication Technologies, Coimbatore, India.
Savaliya, J., Mistry, V., & Desai, M. (2019). Real Time Road Sign Recognition System For Autonomous Car. International Journal of Information and Computing Science, 6(4), 394-406.
Sener, I. N., Zmud, J., & Williams, T. (2019). Measures of baseline intent to use automated vehicles: A case study of Texas cities. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 66-77.
Shachak, A., Kuziemsky, C., & Petersen, C. (2019). Beyond TAM and UTAUT: Future directions for HIT implementation research. Journal of Biomedical Informatics, 100, 103315.
Shen, C.-w., Chen, M., & Wang, C.-c. (2019). Analyzing the trend of O2O commerce by bilingual text mining on social media. Computers in Human Behavior, 101, 474-483.
Shiau, S. J., Huang, C.-Y., Yang, C.-L., & Juang, J.-N. (2018). A derivation of factors influencing the innovation diffusion of the OpenStreetMap in STEM education. Sustainability, 10(10), 3447.
Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach: O'Reilly Media, Inc.
Singh, N., Roy, N., & Gangopadhyay, A. (2019). Analyzing the emotions of crowd for improving the emergency response services. Pervasive and Mobile Computing, 58, 101018.
Souders, D., & Charness, N. (2016). Challenges of older drivers’ adoption of advanced driver assistance systems and autonomous vehicles. Paper presented at the International Conference on Human Aspects of IT for the Aged Population.
Spyropoulou, I., Penttinen, M., Karlaftis, M., Vaa, T., & Golias, J. (2008). ITS solutions and accident risks: Prospective and limitations. Transport Reviews, 28(5), 549-572.
Sukanya, M., & Biruntha, S. (2012). Techniques on text mining. Paper presented at the 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT).
Sun, Y., Liang, C., & Chang, C.-C. (2020). Online social construction of Taiwan's rural image: Comparison between Taiwanese self-representation and Chinese perception. Tourism Management, 76, 103968.
Talukder, M. S., Chiong, R., Bao, Y., & Malik, B. H. (2019). Acceptance and use predictors of fitness wearable technology and intention to recommend: An empirical study. Industrial Management & Data Systems, 119(1), 170-188.
Temme, D., Kreis, H., & Hildebrandt, L. (2010). A comparison of current PLS path modeling software: Features, ease-of-use, and performance. In Vincenzo Esposito Vinzi, Wynne W. Chin, Jörg Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares (pp. 737-756): Springer.
Teo, T. S., Lim, V. K., & Lai, R. Y. (1999). Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1), 25-37.
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 125-143.
Triandis, H. C. (1979). Values, attitudes, and interpersonal behavior. Paper presented at the Nebraska Symposium on Motivation.
Tsao, C., Shieh, J., & Jan, Y. (2009). The study on usage intentions of property service providers for property management system: Application of UTAUT model. Journal of Business Administration, 80, 33-66.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425-478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36, 157-178.
Wang, Y., Li, H., & Wu, Z. (2019). Attitude of the Chinese public toward off-site construction: A text mining study. Journal of Cleaner Production, 238, 117926.
Witten, I. H. (2005). Text mining. In M. P. Singh (Ed.), The Practical Handbook of Internet Computing (pp. 4-1-4-13). Washington, DC: Chapman and Hall/CRC.
Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65.
Xu, Z., Zhang, K., Min, H., Wang, Z., Zhao, X., & Liu, P. (2018). What drives people to accept automated vehicles? Findings from a field experiment. Transportation Research Part C: Emerging Technologies, 95, 320-334.
Zarrinkalam, F., Faralli, S., Piao, G., & Bagheri, E. (2020). Extracting, Mining and Predicting Users' Interests from Social Media. Foundations and Trends® in Information Retrieval, 14(5), 445-617.
Zhang, Y. (2019). Evaluating the Factual Correctness for Abstractive Summarization. In CS230 Project. California: Stanford University.