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NTIS 바로가기디지털융복합연구 = Journal of digital convergence, v.18 no.2, 2020년, pp.249 - 259
프란시스 조셉 코스텔로 (성균관대학교 경영대학) , 이건창 (성균관대학교 글로벌경영학과)
This study presents a recently obtained social media data set based upon the case study of Electric Vehicles (EV) and looks to implement a sentiment analysis (SA) in order to gain insights. This study uses two methods in order to fully analyze the public's sentiment on EVs. First, we implement a SA ...
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X. Tian, Y. Geng, S. Zhong, J. Wilson, C. Gao, W. Chen & H. Hao. (2018). A bibliometric analysis on trends and characters of carbon emissions from transport sector. Transportation Research Part D: Transport and Environment, 59(December 2017) 1-10. https://doi.org/10.1016/j.trd.2017.12.009
W. He, X. Tian, R. Tao, W. Zhang, G. Yan & V. Akula. (2017). Application of social media analytics: A case of analyzing online hotel reviews. Online Information Review, 41(7), 921-935. https://doi.org/10.1108/OIR-07-2016-0201
T. Carpenter (2015). Measuring and Mitigating Electric Vehicle Adoption Barriers. PhD thesis, Waterloo, Ontario.
J. Kim, M. Han, Y. Lee & Y. Park. (2016). Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map. Expert Systems with Applications, 57, 311-323. https://doi.org/10.1016/j.eswa.2016.03.043
J. Li & H. Liu. (2017). Challenges of Feature Selection for Big Data Analytics. IEEE Computer Society, (March), 9-15. https://doi.org/10.1109/MIS.2017.38
M. N. Injadat, F. Salo & A. B. Nassif. (2016). Data mining techniques in social media: A survey. Neurocomputing, 214, 654-670. https://doi.org/10.1016/j.neucom.2016.06.045
B. Li, K. C. C. Chan, C. Ou & S. Ruifeng. (2017). Discovering public sentiment in social media for predicting stock movement of publicly listed companies. Information Systems, 69, 81-92. https://doi.org/10.1016/j.is.2016.10.001
N. F. F. da Silva, E. R. Hruschka & E. R. Hruschka. (2014). Tweet sentiment analysis with classifier ensembles. Decision Support Systems, 66, 170-179. https://doi.org/10.1016/j.dss.2014.07.003
H. Yuan, R. Y. K. Lau & W. Xu. (2016). The determinants of crowdfunding success: A semantic text analytics approach. Decision Support Systems, 91. https://doi.org/10.1016/j.dss.2016.08.001
C. Dhaoui, C. M. Webster & L. P. Tan. (2017). Social media sentiment analysis: lexicon versus machine learning. Journal of Consumer Marketing, 34(6), 480-488. https://doi.org/10.1108/JCM-03-2017-2141
A. Ortigosa, J. M. Martin & R. M. Carrol. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior, 31(1), 527-541. https://doi.org/10.1016/j.chb.2013.05.024
T. W. Rinker. (2018). sentimentr: Calculate Text Polarity Sentiment version 2.6.1. Retrieved from. http://github.com/trinker/sentimentr
C. T. Tran, M. Zhang, P. Andreae, B. Xue & L. T. Bui. (2018). Improving performance of classification on incomplete data using feature selection and clustering. Applied Soft Computing Journal, 73, 848-861. https://doi.org/10.1016/j.asoc.2018.09.026
M. Tutkan, M. C. Ganiz & S. Akyokus. (2016). Helmholtz principle based supervised and unsupervised feature selection methods for text mining. Information Processing and Management, 52(5), 885-910. https://doi.org/10.1016/j.ipm.2016.03.007
K. Seddig, P. Jochem & W. Fichtner. (2017). Integrating renewable energy sources by electric vehicle fleets under uncertainty. Energy, 141, 2145-2153. https://doi.org/10.1016/j.energy.2017.11.140
M. Neaimeh, S. D. Salisbury, G. A. Hill, P. T. Blythe, D. R. Scoffield & J. E. Francfort. (2017). Analysing the usage and evidencing the importance of fast chargers for the adoption of battery electric vehicles. Energy Policy, 108, 474-486. https://doi.org/10.1016/j.enpol.2017.06.033
D. Connolly. (2017). Economic viability of electric roads compared to oil and batteries for all forms of road transport. EnergyStrategy Reviews. https://doi.org/10.1016/j.esr.2017.09.005
L. H. Bjornsson & S. Karlsson. (2017). Electrification of the two-car household: PHEV or BEV? Transportation Research Part C: Emerging Technologies, 85(October), 363-376. https://doi.org/10.1016/j.trc.2017.09.021
I. H. Witten, E. Frank & M. A. Hall. (2011). Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.). Burlington, MA: Morgan Kaufmann Publishers Inc. https://doi.org/10.1016/B978-0-12-374856-0.00001-8
M. A. Hall. (1999). Correlation-based feature selection for machine learning.
R. J. Quinlan. (1986). Induction of decision trees. Machine Learning, 1(1), 81-106. https://doi.org/10.1007/BF00116251
G. Wang, J. Sun, J. Ma, K. Xu & J. Gu (2014). Sentiment classification: The contribution of ensemble learning. DecisionSupport Systems, 57, 77-93. https://doi.org/10.1016/j.dss.2013.08.002
R. Togo, K. Magota, T. Shiga, K. Hirata, I. Tsujino, M. Haseyama & T. Ogawa (2018). Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps. Computers in Biology and Medicine, 104(August 2018), 81-86. https://doi.org/10.1016/j.compbiomed.2018.11.008
A. Onan & S. Korukoglu (2017). A feature selection model based on genetic rank aggregation for text sentiment classification. Journal of Information Science, 43(1), 25-38. https://doi.org/10.1177/0165551515613226
F. Wang, T. Xu, T. Tang, M. Zhou & H. Wang (2017). Bilevel Feature Extraction-Based Text Mining for Fault Diagnosis of Railway Systems. IEEE Transactions on Intelligent Transportation Systems, 18(1), 49-58. https://doi.org/10.1109/TITS.2016.2521866
L. M. Abualigah, A. T.Khader, M. A. Al-Betar, & O. A. Alomari. (2017). Text feature selection with a robust weight schemeand dynamic dimension reduction to text document clustering. Expert Systemswith Applications, 84, 24-36. https://doi.org/10.1016/j.eswa.2017.05.002
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