Fig. 1. Procedure of this study
Table 1. Emotion mining studies
Table 2. Confusion matrix
Table 3. The number of selected features
Table 4. Tweet results
Table 5. SemEval 2007 results
Table 6. T-test Result of Accuracy
References
- J. A. Balazs & J. D. Velasquez. (2016). Opinion mining and information fusion: a survey. Information Fusion, 27, 95-110. https://doi.org/10.1016/j.inffus.2015.06.002
- H. L. Yang & Q. F. Lin. (2018). Opinion mining for multiple types of emotion-embedded products/services through evolutionary strategy. Expert Systems with Applications, 99, 44-55. https://doi.org/10.1016/j.eswa.2018.01.022
- M. V. Mantyla, D. Graziotin & M. Kuutila. (2018). The evolution of sentiment analysis-A review of research topics, venues, and top cited papers. Computer Science Review, 27, 16-32. https://doi.org/10.1016/j.cosrev.2017.10.002
- Y. Liu, J. W. Bi & Z. P. Fan. (2017). Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms. Expert Systems with Applications, 80, 323-339. https://doi.org/10.1016/j.eswa.2017.03.042
- T. Danisman & A. Alpkocak. (2008, April). Feeler: Emotion classification of text using vector space model. In AISB 2008 Convention Communication, Interaction and Social Intelligence (Vol. 1, p. 53).
- C., Strapparava & R. Mihalcea. (2007). Semeval-2007 task 14: Affective text. In Proceedings of the 4th international workshop on semantic evaluations (pp. 70-74). Association for Computational Linguistics.
- N. Gupta, M. Gilbert & G. D. Fabbrizio. (2013). Emotion detection in email customer care. Computational Intelligence, 29(3), 489-505. https://doi.org/10.1111/j.1467-8640.2012.00454.x
- M. Hasan, E. Agu & E. Rundensteiner. (2014). Using hashtags as labels for supervised learning of emotions in twitter messages. In ACM SIGKDD Workshop on Health Informatics, New York, USA.
- C. Quan & F. Ren. (2016). Weighted high-order hidden Markov models for compound emotions recognition in text. Information Sciences, 329, 581-596. https://doi.org/10.1016/j.ins.2015.09.050
- M. A. Hall. (1999). Correlation-based feature selection for machine learning.
- M. Robnik-Sikonja & I. Kononenko. (2003). Theoretical and empirical analysis of ReliefF and RReliefF. Machine learning, 53(1-2), 23-69. https://doi.org/10.1023/A:1025667309714
- D. R. Cox. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society. Series B (Methodological), 215-242.
- S. K. Murthy. (1998). Automatic construction of decision trees from data: A multi-disciplinary survey. Data mining and knowledge discovery, 2(4), 345-389. https://doi.org/10.1023/A:1009744630224
- E. C. Bae & K. C. Lee. (2016). Predicting Stock Liquidity by Using Ensemble Data Mining Methods", Journal of The Korea Society of computer and Information, 21(6), 9-19, https://doi.org/10.9708/JKSCI.2016.21.6.009
- S. Park, K. M. Yang & S. B. Cho. (2013). A Hierarchical CPV Solar Generation Tracking System based on Modular Bayesian Network. Journal of KIISE: Software and Applications, 41.
- V. Vapnik. (2013). The nature of statistical learning theory. Springer science & business media.
- M. H. Song, J. Lee, S. P. Cho & K. J. Lee. (2005). SVM Classifier for the Detection of Ventricular Fibrillation, The Institute of Electronics Engineers of Korea - System and Control, 42(5), 27-34.
- M. Ballings, D. Van den Poel, N. Hespeels & R. Gryp. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42(20), 7046-7056. https://doi.org/10.1016/j.eswa.2015.05.013
- D. H. Wolpert. (1992). Stacked generalization. Neural networks, 5(2), 241-259. https://doi.org/10.1016/S0893-6080(05)80023-1
- J. H. Lee & J. G. Baek. (2018). RTC(Real-Time Contrast) Control Chart using Random Forest based Multi-Class Classifier, Journal of the Korean Institute of Industrial Engineers, 44(4), 306-315. https://doi.org/10.7232/JKIIE.2018.44.4.306
- T. K. Ho. (1998). The Random Subspace Method for Constructing Decision Forests, IEEE Trans. Pattern Analysis and Machine Intelligence, 20(8), 832-844. https://doi.org/10.1109/34.709601
- W. Wang, L. Chen, K. Thirunarayan & A. P. Sheth. (2012). Harnessing twitter big data for automatic emotion identification. In Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom), IEEE, 587-592.
- A. Yadollahi, A. G. Shahraki & O. R. Zaiane. (2017). Current state of text sentiment analysis from opinion to emotion mining. ACM Computing Surveys (CSUR), 50(2), 25.
- S. Arlot & A. Celisse. (2010). A survey of cross-validation procedures for model selection. Statistics surveys, 4, 40-79. https://doi.org/10.1214/09-SS054