최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Frontiers in robotics and AI, v.5, 2018년, pp.138 -
Tsapatsoulis, Nicolas , Djouvas, Constantinos
The era of big data has, among others, three characteristics: the huge amounts of data created every day and in every form by everyday people, artificial intelligence tools to mine information from those data and effective algorithms that allow this data mining in real or close to real time. On the ...
Agarwal A. Xie B. Vovsha I. Rambow O. Passonneau R. ( 2011 ). Sentiment analysis of twitter data , in Proceedings of the Workshop on Languages in Social Media, LSM'11, Association for Computational Linguistics ( Portland, OR ), 30 – 38 .
Aizawa A. ( 2003 ). An information-theoretic perspective of tf-idf measures . Inform. Process. Manage. 39 , 45 – 65 . 10.1016/s0306-4573(02)00021-3
Barbosa L. Feng J. ( 2010 ). Robust sentiment detection on twitter from biased and noisy data , in Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING'10, Association for Computational Linguistics ( Beijing ), 36 – 44 .
Borromeo R. M. Toyama M. ( 2014 ). Automatic vs. crowdsourced sentiment analysis , in Proceedings of the 19th International Database Engineering & Applications Symposium, IDEAS '15 ( Yokohama: ACM ), 90 – 95 .
Brabham D. C. ( 2009 ). Crowdsourcing the public participation process for planning projects . Plan. Theory 8 , 242 – 262 . 10.1177/1473095209104824
Brown P. F. deSouza P. V. Mercer R. L. Pietra V. J. D. Lai J. C. ( 1992 ). Class-based n-gram models of natural language . Comput. Linguist. 18 , 467 – 479 .
Buzzwords ( 2019 ). Top 10 it & Technology Buzzwords You Won't be Able to Avoid in 2019 . Available online at: https://www.datapine.com/blog/technology-buzzwords/ (Accessed December 01, 2018).
Cabrall C. D. Lu Z. Kyriakidis M. Manca L. Dijksterhuis C. Happee R. . ( 2018 ). Validity and reliability of naturalistic driving scene categorization judgments from crowdsourcing . Accid. Analy. Prevent. 114 , 25 – 33 . 10.1016/j.aap.2017.08.036 28911877
Cowie R. Douglas-Cowie E. Tsapatsoulis N. Votsis G. Kollias S. Fellenz W. ( 2001 ). Emotion recognition in human-computer interaction . IEEE Signal Process. Magaz. 18 , 32 – 80 . 10.1109/79.911197
De Mauro A. Greco M. Grimald M. ( 2015 ). A formal definition of big data based on its essential features . Lib. Rev. 65 , 122 – 135 . 10.1108/LR-06-2015-0061
Denn N. Zuping Z. Damien H. Long J. ( 2015 ). A lexicon-based approach for hate speech detection . Int. J. Mult. Ubiquit. Eng. 10 , 215 – 230 . 10.14257/ijmue.2015.10.4.21
Doan A. Ramakrishnan R. Halevy A. Y. ( 2011 ). Crowdsourcing systems on the world-wide web . Commun. ACM 54 , 86 – 96 . 10.1145/1924421.1924442
ENCASE ( 2016 ). Enhancing Security and Privacy in the Social Web . Available online at: https://encase.socialcomputing.eu/ (Accessed December 01, 2018).
fastText ( 2018 ). Library for Fast Text Representation and Classification . Availble online at: https://github.com/facebookresearch/fastText/ (Accessed December 01, 2018).
Figure-eight (n.d.). We make ai work in the real world . https://www.figure-eight.com/ (Accessed December 01, 2018).
Finin T. Murnane W. Karandikar A. Keller N. Martineau J. Dredze M. ( 2010 ). Annotating named entities in twitter data with crowdsourcing , in Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk, CSLDAMT '10, Association for Computational Linguistics ( Los Angeles, CA ), 80 – 88 .
Founta A. Djouvas C. Chatzakou D. Leontiadis I. Blackburn J. Stringhini G. ( 2018 ). Large scale crowdsourcing and characterization of twitter abusive behavior , in Proceedings of the 12th International Conference on Web and Social Media, ICWSM 2018. Stanford, CA , 491 – 500 .
Giannoulakis S. Tsapatsoulis N. ( 2016a ). Defining and identifying stophashtags in instagram , in Proceedings of the 2nd INNS Conference on Big Data 2016, INNS Big Data 2016 ( Thessaloniki ), 304 – 313 .
Giannoulakis S. Tsapatsoulis N. ( 2016b ). Evaluating the descriptive power of instagram hashtags . J. Innov. Digital Ecosyst. 3 , 114 – 129 . 10.1016/j.jides.2016.10.001
Giannoulakis S. Tsapatsoulis N. Ntalianis K. S. ( 2017 ). Identifying image tags from instagram hashtags using the HITS algorithm , in DASC/PiCom/DataCom/CyberSciTech ( Orlando, FL : IEEE ), 89 – 94 .
Giuffrida M. Chen F. Scharr H. Tsaftaris S. ( 2018 ). Citizen crowds and experts: observer variability in image-based plant phenotyping . Plant Methods 14 , 1 – 14 . 10.1186/s13007-018-0278-7 29321806
Hamdan H. Bellot P. Bechet F. ( 2015 ). lsislif: Feature extraction and label weighting for sentiment analysis in twitter , in Proceedings of the 9th International Workshop on Semantic Evaluation, Association for Computational Linguistics (Denver, CO), 568 – 573 .
Hashem I. A. T. Yaqoob I. Anuar N. B. Mokhtar S. Gani A. Ullah Khan S. ( 2015 ). The rise of “big data” on cloud computing . Inform. Syst. 47 , 98 – 115 . 10.1016/j.is.2014.07.006
Hate base (n.d.). The world's largest structured repository of regionalized, multilingual hate speech . Available online at: https://www.hatebase.org/ (Accessed December 02, 2018).
Howe J. ( 2008 ). Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business . New York, NY : Crown Publishing Group .
Hsueh P.-Y. Melville P. Sindhwani V. ( 2009 ). Data quality from crowdsourcing: a study of annotation selection criteria , in Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing, HLT '09, Association for Computational Linguistics ( Boulder, CO ), 27 – 35 .
Joulin A. Grave E. Bojanowski P. Mikolov T. ( 2017 ). Bag of tricks for efficient text classification , in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics ( Valencia: Association for Computational Linguistics ), 427 – 431 .
Karpouzis K. Tsapatsoulis N. Kollias S. D. ( 2000 ). Moving to continuous facial expression space using the MPEG-4 facial definition parameter (FDP) set , in Proceedings of Human Vision and Electronic Imaging, SPIE, Vol. 3959 ( San Francisco, CA : SPIE ), 443 – 450 .
Layton R. Watters P. Dazeley R. ( 2011 ). Recentred local profiles for authorship attribution . Nat. Langu. Eng. 18 , 293 – 312 . 10.1017/S1351324911000180
Le Q. Mikolov T. ( 2014 ). Distributed representations of sentences and documents , in Proceedings of the 31st International Conference on International Conference on Machine Learning - Vol. 32, ICML'14, JMLR.org ( Beijing ), II–1188–II–1196.
Legomena (n.d.). Hapax Legomenon . Availble online at: https://en.wikipedia.org/wiki/Hapax_legomenon (Accessed December 01, 2018).
Maas A. L. Daly R. E. Pham P. T. Huang D. Ng A. Y. Potts C. ( 2011 ). Learning word vectors for sentiment analysis , in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Vol. 1, HLT '11, Association for Computational Linguistics ( Portland, OR ), 142 – 150 .
Mac Kim S. Calvo R. A. ( 2010 ). Sentiment analysis in student experiences of learning , in Proceedings of the 3rd International Conference on Educational Data Mining ( Pittsburgh, PA ), 111 – 120 .
Machedon R. Rand W. Joshi Y. ( 2013 ). Automatic crowdsourcing-based classification of marketing messaging on twitter , in Proceedings of the 2013 International Conference on Social Computing, SOCIALCOM '13 ( Alexandria, VA : IEEE Computer Society ), 975 – 978 .
Maier-Hein L. Mersmann S. Kondermann D. Bodenstedt S. Sanchez A. Stock C. . ( 2014 ). Can masses of non-experts train highly accurate image classifiers? a crowdsourcing approach to instrument segmentation in laparoscopic images , in Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention ( Boston, MA ), 438 – 445 .
Mikolov T. Sutskever I. Chen K. Corrado G. Dean J. ( 2013 ). Distributed representations of words and phrases and their compositionality , in Proceedings of the 26th International Conference on Neural Information Processing Systems - Vol. 2, NIPS'13 ( Lake Tahoe, NV ), 3111 – 3119 .
Mitry D. Zutis K. Dhillon B. Peto T. Hayat S. Khaw K.-T. . ( 2016 ). The accuracy and reliability of crowdsource annotations of digital retinal images . Trans. Vis. Sci. Technol. 5 : 6 . 10.1167/tvst.5.5.6 27668130
MTurk (n.d.). Amazon Mechanical Turk . Available online at: https://www.mturk.com/ (Accessed December 01, 2018).
Narayanan V. Arora I. Bhatia A. ( 2013 ). Fast and accurate sentiment classification using an enhanced naive bayes model , in Proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning — IDEAL 2013 - Vol. 8206 , IDEAL 2013, New York, NY : Springer-Verlag Inc , 194 – 201 .
NLTK (n.d.). Natural Language Toolkit . Available online at: https://www.nltk.org/ (Accessed December 02, 2018).
Ntalianis K. Tsapatsoulis N. ( 2016 ). Wall-content selection in social media: a relevance feedback scheme based on explicit crowdsourcing , in Proceedings of the 9th IEEE International Conference on Cyber, Physical, and Social Computing, CPSCom'2016 ( Chengdu : IEEE ), 534 – 539 .
Ntalianis K. Tsapatsoulis N. Doulamis A. Matsatsinis N. ( 2014 ). Automatic annotation of image databases based on implicit crowdsourcing, visual concept modeling and evolution . Mult. Tools Appl. 69 , 397 – 421 . 10.1007/s11042-012-0995-2
Pak A. Paroubek P. ( 2010 ). Twitter as a corpus for sentiment analysis and opinion mining , in Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC'10 ( Valletta : European Language Resources Association (ELRA) ), 1320 – 1326 .
Pedregosa F. Varoquaux G. Gramfort A. Michel V. Thirion B. Grisel O. ( 2011 ). Scikit-learn: machine learning in python . J. Mach. Learn. Res. 12 , 2825 – 2830 .
Prusa J. D. Khoshgoftaar T. M. Dittman D. J. ( 2015 ). Impact of feature selection techniques for tweet sentiment classification , in Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference ( Hollywood, FL ), 299 – 304 .
Salton G. Wong A. Yang C. S. ( 1975 ). A vector space model for automatic indexing . Commun. ACM 18 , 613 – 620 .
Shirbhate A. G. Deshmukh S. N. ( 2016 ). Feature extraction for sentiment classification on twitter data . Int. J. Sci. Res. 5 , 2183 – 2189 .
Socher R. Perelygin A. Wu J. Y. Chuang J. Manning C. D. Ng A. Y. ( 2013 ). Recursive deep models for semantic compositionality over a sentiment treebank , in Proceedings of 2013 Conference on Empirical Methods in Natural Language Processing ( Seattle, WA ), 1631 – 1642 .
Stavrianou A. Brun C. Silander T. Roux C. ( 2014 ). Nlp-based feature extraction for automated tweet classification , in Proceedings of the 1st International Conference on Interactions Between Data Mining and Natural Language Processing - Vol. 1202 DMNLP'14 (Nancy), 145 – 146 .
Stopwords (n.d.). Stop Words . Availble online at: https://en.wikipedia.org/wiki/Stop_words (Accessed December 02, 2018).
Surowiecki J. ( 2005 ). The Wisdom of Crowds . New York, NY : Anchor .
TextBlob (n.d.). Textblob: Simplified Text Processing . Available online at: https://textblob.readthedocs.io/en/dev/ (Accessed December 01, 2018).
Tsapatsoulis N. Djouvas C. ( 2017 ). Feature extraction for tweet classification: Do the humans perform better? in Semantic and Social Media Adaptation and Personalization (SMAP), 2017 12th In'l Workshop on ( Bratislava : IEEE ), 53 – 58 .
TurKit (n.d.). Iterative Tasks on Mechanical Turk . Available online at: http://up.csail.mit.edu/turkit/ (Accessed December 01, 2018).
TweetTokenizer (n.d.). Tweet Tokenize Package . Available online at: http://www.nltk.org/api/nltk.tokenize.html Accessed: 2018-12-02.
uTest (n.d.). utest . Available online at: https://www.utest.com/ (Accessed December 01, 2018).
Vukovic M. ( 2009 ). Crowdsourcing for enterprises , in 2009 Congress on Services - I ( Los Angeles, CA ), 686 – 692 .
Yaqoob I. Hashem I. A. T. Gani A. Mokhtar S. Ahmed E. Anuar N. B. ( 2016 ). Big data: from beginning to future . Int. J. Inform. Manage. 36 , 1231 – 1247 . 10.1016/j.ijinfomgt.2016.07.009
Zafarani R. Abbasi M. A. Liu H. ( 2014 ). Social Media Mining: An Introduction . Cambridge, UK : Cambridge University Press .
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.