Prediction of Citizens’ Emotions on Home Mortgage Rates Using Machine Learning Algorithms

키워드

참고문헌 (55)

1. Alkan A, "Automatic seizure detection in EEG using logistic regression and artificial neural network", Journal of Neuroscience Methods, 148(2), pp.167~176 (2005)[DOI]
2. Almeida AM, "Applying multi-label techniques in emotion identification of short texts", Neurocomputing, 320, pp.35~46 (2018)[DOI]
3. Arnold MB, "Emotion and personality. Vol. I" (1960), Psychological aspects
4. Avetisyan H, "Overview of existing algorithms for emotion classification. Uncertainties in evaluations of accuracies", Journal of Physics: Conference Series, 772(1), pp.012039~ (2016)[DOI]
5. Bravo-Marquez F, "Combining strengths, emotions and polarities for boosting Twitter sentiment analysis", Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (2013)
6. Random forests, "Breiman L.", UC Berkeley, ~, pp.TR567 (1999)
7. Burnap P, "Machine classification and analysis of suicide-related communication on twitter", Proceedings of the 26th ACM conference on hypertext & social media, pp.75~84 (2015)
8. Burnap P, "Cyber hate speech on twitter: An application of machine classification and statistical modeling for policy and decision making", Policy & Internet, 7(2), pp.223~242 (2015)[DOI]
9. Cannuscio CC, "Housing strain, mortgage foreclosure, and health", Nursing outlook, 60(3), pp.134~142 (2012)[DOI]
10. Casale S, "Speech emotion classification using machine learning algorithms", The IEEE International Conference on Semantic Computing, pp.158~165 (2008), IEEE
11. Chen YL, "Emotion classification of YouTube videos", Decision Support Systems, 101, pp.40~50 (2017)[DOI]
12. Colneriĉ N, "Emotion Recognition on Twitter: Comparative Study and Training a Unison Model", IEEE Transactions on Affective Computing (2018)
13. Damrongsakmethee T, "Data Mining and Machine Learning for Financial Analysis", Indian Journal of Science and Technology, 10(39) (2017)[DOI]
14. Dong L, "Nonlinear methodologies for identifying seismic event and nuclear explosion using random forest, support vector machine, and naive Bayes classification", Abstract and Applied Analysis, 2014 (2014)
15. Hindawi. Du W, "Building decision tree classifier on private data", Proceedings of the IEEE international conference on Privacy, 14, pp.1~8 (2002)
16. Ekman P, "An argument for basic emotions", Cognition & emotion, 6(3-4), pp.169~200 (1992)[DOI]
17. Ekman P, "Constants across cultures in the face and emotion", Journal of personality and social psychology, 17(2), pp.124~ (1971)[DOI]
18. Frijda NH, "The emotions" (1986), Cambridge University Press
19. Gievska S, "The impact of affective verbal content on predicting personality impressions in youtube videos", Proceedings of the 2014 ACM Multi Media on Workshop on Computational Personality Recognition, pp.19~22 (2014)
20. Hastie T, "Multi-class adaboost", Statistics and its Interface, 2(3), pp.349~360 (2009)[DOI]
21. Hu W, "Adaboost-based algorithm for network intrusion detection", IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(2), pp.577~583 (2008)[DOI]
22. Gerardi K, "Subprime outcomes: Risky mortgages, homeownership experiences, and foreclosures", ~ (2007)
23. Gil GB, "Combining machine learning techniques and natural language processing to infer emotions using Spanish Twitter corpus", International Conference on Practical Applications of Agents and Multi-Agent Systems, pp.149~157 (2013)
24. Gislason PO, "Random forests for land cover classification", Pattern Recognition Letters, 27(4), pp.294~300 (2006)[DOI]
25. Twitter sentiment classification using distant supervision, "Go A", ~, pp.CS224N Project Report, Stanford. 1(12) (2009)
26. Jang E, "Emotion classification by machine learning algorithm using physiological signals", Proc. of Computer Science and Information Technology. Singapore, 25, pp.1~5 (2012)
27. 임좌상, "한국어 트위터의 감정 분류를 위한 기계학습의 실증적 비교", 멀티미디어학회논문지, 17(2), pp.232~239 (2014), 한국멀티미디어학회[DOI]
28. Luo C, "A deep learning approach for credit scoring using credit default swaps", Engineering Applications of Artificial Intelligence, 65, pp.465~470 (2017)[DOI]
29. Mohammad SM, "Emotional tweets. Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1:Proceedings of the main conference and the shared task, and Volume 2", Proceedings of the Sixth International Workshop on Semantic Evaluation, 1-2, pp.246~255 (2012)
30. Moore-Kochlacs CE, "Extracellular electrophysiology with close-packed recording sites:spike sorting and characterization", ~ (2016)
31. Neethu MS, "Sentiment analysis in twitter using machine learning techniques", 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp.1~5 (2013)
32. Ng AY, "On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes", Advances in neural information processing systems, pp.841~848 (2002)
33. Ortony A, "What's basic about basic emotions?", Psychological review, 97(3), pp.315~ (1990)[DOI]
34. Pal M, "Random forest classifier for remote sensing classification", International Journal of Remote Sensing, 26(1), pp.217~222 (2005)[DOI]
35. Pal M, "Support vector machine‐based feature selection for land cover classification : a case study with DAIS hyperspectral data", International Journal of Remote Sensing, 27(14), pp.2877~2894 (2006)[DOI]
36. Peterson LE, "K-nearest neighbor", Scholarpedia, 4(2), pp.1883~ (2009)[DOI]
37. Plutchik R, "Emotion, psychopathology, and psychotherapy", pp.3~41 (1990)
38. Plutchik R., "The emotions" (1991), University Press of America
39. Roberts K, "EmpaTweet: Annotating and Detecting Emotions on Twitter", LREC, 12, pp.3806~3813 (2012)[DOI]
40. Rodriguez-Galiano VF, "An assessment of the effectiveness of a random forest classifier for land-cover classification", ISPRS Journal of Photogrammetry and Remote Sensing, 67, pp.93~104 (2012)[DOI]
41. Safavian SR, "A survey of decision tree classifier methodology", IEEE transactions on systems, man, and cybernetics, 21(3), pp.660~674 (1991)[DOI]
42. Sitthi A, "Exploring land use and land cover of geotagged social-sensing images using naive bayes classifier", Sustainability, 8(9), pp.921~ (2016)[DOI]
43. Strapparava C, "Wordnet affect : an affective extension of wordnet", Lrec, 4, pp.1083~1086 (2004)[DOI]
44. Subasi A, "Classification of EEG signals using neural network and logistic regression", Computer methods and programs in biomedicine, 78(2), pp.87~99 (2005)[DOI]
45. Tan S, "An effective refinement strategy for KNN text classifier", Expert Systems with Applications, 30(2), pp.290~298 (2006)[DOI]
46. Tang D, "Natural Language Processing and Chinese Computing", pp.212~223 (2013), Springer
47. Tang D, "Coooolll: A deep learning system for twitter sentiment classification", Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp.208~212 (2014)
48. Vatsavai RR, "Machine learning approaches for high-resolution urban land cover classification: a comparative study", Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications, pp.11~ (2011)
49. Velásquez F, "Empirical study of machine learning based approach for opinion mining in tweets", Mexican international conference on Artificial intelligence, pp.1~14 (2012)
50. Wang W, "Harnessing twitter “big data” for automatic emotion identification. Privacy, Security, Risk and Trust (PASSAT)", 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom), pp.587~592 (2012), IEEE
51. Wei Y, "FPGA implementation of AdaBoost algorithm for detection of face biometrics", 2004 IEEE International Workshop on, pp.S1~S6 (2004)
52. Wen S, "Emotion classification in microblog texts using class sequential rules", Twenty-Eighth AAAI conference on artificial intelligence (2014)
53. Yunda Wu, "New Blind Steganalysis Framework Combining Image Retrieval and Outlier Detection", KSII Transactions on Internet and Information Systems, 10(12), pp.6206~6219 (2016), 한국인터넷정보학회
54. Yadav P, "Speech Emotion Classification using Machine Learning", International Journal of Computer Applications, 118(13), pp.44~47 (2015)[DOI]
55. Zhang Y, "Analysis of structural brain MRI and multi-parameter classification for Alzheimer’s disease", Biomedical Engineering, 2018, pp.63(4), 427~437

관련 논문AI추천