Phát hiện cảm xúc trong văn bản trên mạng xã hội bằng bộ phân loại tập hợp mới dựa trên Ước lượng Parzen theo cấu trúc cây (TPE)

Ekman P (1972) Universal and cultural differences in facial expression of emotion. In: Nebraska symposium on motivation, vol 19, pp 207–284 Colombetti G (2009) From affect programs to dynamical discrete emotions. Philos Psychol 22(4):407–425 Roseman IJ (1991) Appraisal determinants of discrete emotions. Cogn Emot 5(3):161–200 Ekman P (1992) An argument for basic emotions. Cogn Emot 6(3–4):169–200 Chaffar S, Inkpen D (2011) Using a heterogeneous dataset for emotion analysis in text. In: Canadian conference on artificial intelligence, pp 62–67 Perikos I, Hatzilygeroudis I (2016) Recognizing emotions in text using ensemble of classifiers. Eng Appl Artif Intell 51:191–201 Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113 De Marneffe M-C, MacCartney B, Manning CD (2006) Generating typed dependency parses from phrase structure parses. In: Proceedings of LREC, pp 449–454 Strapparava C, Valitutti A (2004) Wordnet affect: an affective extension of wordnet. In: Lrec, pp 1083–1086 Xuegong Z (2000) Introduction to statistical learning theory and support vector machines. Acta Autom Sin 26(1):32–42 Wu X et al (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37 Murray AF (1995) Applications of neural networks. The University of Edinburgh UK: Springer, Boston, MA Rokach L, Maimon O (2005) Top-down induction of decision trees classifiers—a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 35(4):476–487 Zhang Y, Jin R, Zhou Z-H (2012) Understanding bag-of-words model: a statistical framework. Int J Mach Learn Cybern 1(1–4):43–52 Mikolov T et al (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188–1196 Bergstra JS et al (2011) Algorithms for hyper-parameter optimization. In: Advances in neural information processing systems, pp 2546–2554 Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration. In: International conference on learning and intelligent optimization, pp 507–523 Zhao M, Li J (2018) Tuning the hyper-parameters of CMA-ES with tree-structured Parzen estimators. In: Tenth international conference on advanced computational intelligence (ICACI), pp 613–618 Hinz T et al (2018) Speeding up the hyperparameter optimization of deep convolutional neural networks. Int J Comput Intell Appl 17(2):1850008 Ilievski I et al (2017) Efficient hyperparameter optimization for deep learning algorithms using deterministic RBF surrogates. In: AAAI, pp 822–829 Madrigal F, Maurice C, Lerasle F (2019) Hyper-parameter optimization tools comparison for Multiple Object Tracking applications. Mach Vis Appl 30(2):269–289 Wang S et al (2018) A TPE based inversion of PROSAIL for estimating canopy biophysical and biochemical variables of oilseed rape. Comput Electron Agric 152:350–362 Jones DR (2001) A taxonomy of global optimization methods based on response surfaces. J Glob Optim 21(4):345–383 Quan C, Ren F (2010) Sentence emotion analysis and recognition based on emotion words using Ren-CECps. Int J Adv Intell 2(1):105–117 Balahur A, Hermida JM, Montoyo A (2011) Detecting implicit expressions of sentiment in text based on commonsense knowledge. In: Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis, pp 53–60 Balahur A, Hermida JM, Montoyo A (2012) Building and exploiting emotinet, a knowledge base for emotion detection based on the appraisal theory model. IEEE Trans Affect Comput 3(1):88–101 Badugu S, Suhasini M (2017) Emotion detection on twitter data using knowledge base approach. Int J Comput Appl 162(10):975–978 Danisman T, Alpkocak A (2008) Feeler: emotion classification of text using vector space model. In: AISB 2008 convention communication, interaction and social intelligence, p 53 Quan C, Ren F (2016) Weighted high-order hidden Markov models for compound emotions recognition in text. Inf Sci 329:581–596 Webster JJ, Kit C (1992) Tokenization as the initial phase in NLP. In: Proceedings of the 14th conference on computational linguistics, vol 4, pp 1106–1110 Jivani AG (2011) A comparative study of stemming algorithms. Int J Comput Technol Appl 2(6):1930–1938 Efron B, Tibshirani RJ (1994) An introduction to the bootstrap. CRC Press, Boca Raton ISEAR, [online]. http://emotion-research.net/toolbox/toolboxdatabase.2006-10-13.2581092615. Accessed Sept 2017 Open American National Corpus, [online]. http://www.anc.org/data/masc. Accessed: Sept 2017 The Emotion in Text data set by CrowdFlower, [online]. https://www.crowdflower.com/wpcontent/uploads/2016/07/text_emotion.csv. Accessed Sept 2017