Topics on Information Systems: Short Courses for SBSI 2016
Keywords:
Sentiment Analysis, Multiclass Classification, Text Preprocessing, Machine Learning, Data Visualization, Innovative Business Models, Cloud Computing, LBS Systems, Internet of Things, Wearable Computing, Context-Aware Computing, Ubiquitous ComputingSynopsis
In the edition of Short Courses for SBSI 2016, four short courses were selected from among seventeen proposals submitted through a public call widely divulged through electronic lists of SBC (Brazilian Society of Computing). All the proposals received at least three evaluations carried out by a Committee composed of forty-five Ph.D. professors, who considered criteria such as relevance to the event, public expectation, timeliness and content. SBSI 2016 also innovates by launching, for the first time, the contents of mini-courses in book form, whose four selected proposals constitute its chapters.
Chapter 1 presents the text of the mini-course entitled "Sentiment Analysis Using Multiclass Classification Techniques," which aims to introduce the sentiment analysis process using feature extraction techniques, performing textual preprocessing, feature selection, vectorization, and finally, machine learning, to infer whether an opinion is positive or negative (binary classification) or to infer a rating (multiclass classification).
Chapter 2 presents the text of the mini-course entitled "Keep Calm and Visualize Your Data," which presents a practical approach that reconciles the literature on information visualization and its various available resources in building graphical representations of data that facilitate decision-making and knowledge dissemination processes.
Chapter 3 presents the text of the mini-course entitled "Innovative Business Models in the Era of Cloud Computing," in which the authors present cloud-based business models to provide means and knowledge for the production of an innovative business model. They also present how and where to incubate and accelerate your business, where to find investors, as well as real cases of successful and unsuccessful business models, and the role of cloud computing in this process.
Chapter 4 presents the text of the mini-course entitled "LBS Systems, Internet of Things, and Wearable Computing: Using Context-Aware Computing to Develop 21st Century Applications," which provides an overview of Mobile Computing, Context-Aware Computing, and Ubiquitous/Pervasive Computing, especially Location-Based Systems (LBS), illustrating their potentials, problems, and challenges to become popular and reach end-users more efficiently.
We believe that this material can be widely used by Information Systems professors as a basis for their classes, by researchers for discussing new approaches and as inputs for their current and future research, and by professionals as assistance in their daily practice. We hope that everyone who has access to the content of these mini-courses will make great use of it!
Chapters
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1. Analysis of Sentiments Using Multiclass Classification Techniques
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2. Keep Calm and Visualize Your Data
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3. Innovative Business Models in the Era of Cloud Computing
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4. LBS Systems, Internet of Things, and Wearable Computing: Using Context-Aware Computing to Develop 21st Century Applications
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References
Abowd, G. D. and Dey, A. K. and Brown, P. J. and Davies, N. and M. Smith and P. Steggles (1999) “Towards a better understanding of context and context-awareness,” In: HUC ’99: Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing. Springer-Verlag, pp. 304–307, London, UK.
Aleksy, Markus and Rissanen, Mikko J. and Maczey, Sylvia and Dix, Marcel. (2011). “Wearable Computing in Industrial Service Applications”, Procedia Computer Science, Volume 5, Pages 394-400, ISSN 1877-0509, DOI: 10.1016/j.procs.2011.07.051.
Alexandre, D. S. and Tavares, J. M. R. S. (2007). Factores da percepção visual humana na visualização de dados. Congresso de Métodos Numéricos em Engenharia, XXVIII CILAMCE-Congresso Ibero Latino-Americano sobre Métodos Computacionais em Engenharia,
Aly, M. (2005) “Survey on Multiclass Classification Methods Extensible Algorithms. Neural Networks”, N. November, P. 1–9.
Aurélio (2016). Dicionário Português - Dicionário do Aurélio Online. [link], [accessed on Apr 21].
Baccianella, S., Esuli, A. and Sebastiani, F. (2010) “Sentiwordnet 3.0: an Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining”, Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), V. 0, N. November, P. 2200–2204.
Beineke, P., Hastie, T., Manning, C. and Vaithyanathan, S. (2004) “Exploring Sentiment Summarization”, Proceedings Of The AAAI Spring Symposium On Exploring Attitude And Affect In Text Theories And Applications, V. 07, P. 1–4.
Bill N. A. and Schilit N. and W. R. (1994) “Context-aware computing applications,” In: Proceedings of the Workshop on Mobile Computing Systems and Applications, Santa Cruz, CA, 8(5), pages 85-90, IEEE Computer Society. [Online]. Available: [link].
Brooke, J. (2009) “A Semantic Approach to Automated Text Sentiment Analysis”, Simon Fraser University, V. 26, N. 4, P. 118.
Cairo, A. (2012). The Functional Art: An introduction to information graphics and visualization. New Riders.
Cairo, A. (2014). The Functional Art: An Introduction to Information Graphics and Visualization: Explaining Snow, Minard, and Nightingale. [link], [accessed on Apr 5].
Cambria, E., Schuller, B., Xia, Y.and Havasi, C. (2013) “New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, N. April, P. 15–21.
Card, S. (2003). Information visualization. In: Jacko, J. A.; Sears, A.[Eds.]. . The Human-computer Interaction Handbook. Hillsdale, NJ, USA: L. Erlbaum Associates Inc. p. 544–582.
Card, S. K. and Mackinlay, J. (1997). The structure of the information visualization design space. In Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis ’97). , INFOVIS ’97. IEEE Computer Society. [link], [accessed on May 30].
Card, S. K., Mackinlay, J. D. and Shneiderman, B. [Eds.] (1999). Readings in information visualization: using vision to think. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
Chandra, A.; Fealey, T. (2009). Business incubation in the united states, china and brazil: A comparison of role of government, incubator funding and financial services. International Journal of Entrepreneurship, 13:67 – 86.
Chen, C., Ibekwe-Sanjuan, F., Sanjuan, E. and Weaver, C. (2006) “Visual Analysis of Conflicting Opinions”, IEEE Symposium on Visual Analytics Science and Technology 2006, Vast 2006 - Proceedings, P. 59–66.
Chen, G. and Kotz, D. (2000) “A survey of context-aware mobile computing research,” In: Technical Report TR2000-381 - Dartmouth College. [Online]. Available: [link].
Cleveland, W. S. and McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, v. 79, n. 387, p. 531–554.
Cohen, S. Hochberg, Y. V. (2014). Accelerating Startups: The seed accelerator phenomenon. Available at SSRN 2418000.
Da Silva, C. G. (2014). Visualização de Informação: Introdução e Influências de IHC. In Companion Proceedings of the 13th Brazilian Symposium on Human Factors in Computing Systems. , IHC ’14. Sociedade Brasileira de Computação. [link], [accessed on Apr 21].
Das, S. R and Chen, M. Y. (2001) “Yahoo! For Amazon: Opinion Extraction From Small Talk on the Web. Proceedings of the 8th Asia Pacific Finance Association Annual Conference, V. Xxxiii, N. 2, P. 81–87.
Dave, K., Lawrence, S. and Pennock, D. (2003) “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews”, Proceedings of the 12th International Conference on World Wide Web, P. 519–528.
De Albornoz, J. C., Plaza, L., Gervás, P. and Díaz, A. (2011) “A Joint Model of Feature Mining and Sentiment Analysis for Product Review Rating”, Advances in Information Retrieval, p. 55–66.
Deak, Gabriel. and Curran, Kevin. and Condell, Joan (2012). “A survey of active and passive indoor localisation systems”. In: Computer Communications, 35(16): 1939 – 1954.
Deng, J., Hu, J., Liu, A., and Wu, J. (2010). Research and application of cloud storage. In Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on, pages 1–5.
Dey, A. and Salber, D. and Abowd, G. (2001) “A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications”. [Online]. Available: [link].
Dicio (2016). Dicionário Online de Português. [link], [accessed on Apr 21].
Dictionary. (2011) “Free online dictionary of computing,” In: [Online]. Available: [link].
Distefano, Salvatore and Merlino, Giovanni and Puliafito, Antonio. (2015). “A utility paradigm for IoT: The sensing Cloud, Pervasive and Mobile Computing”, Volume 20, July 2015, Pages 127-144, ISSN 1574-1192, DOI: 10.1016/j.pmcj.2014.09.006.
Dumais, S., Platt, J., Heckerman, D. and Sahami, M. (1998) “Inductive Learning Algorithms and Representations for Text Categorization”, Cikm ’98: Proceedings of the Seventh International Conference on Information and Knowledge Management, P. 148–155.
Ferreira, G. X. (2015). Explorando o Processo de Criação de Visualização de Informação no Ambiente de Dados Abertos Governamentais. Universidade Federal de Itajubá.
Few, S. (2006). The Surest Path to Visual Discovery by Stephen Few - BeyeNETWORK. [link], [accessed on Apr 21].
Few, S. (2013). Data Visualization for Human Perception. The Encyclopedia of Human-Computer Interaction, 2nd Ed.,
Freitas, C. M. D. S., Chubachi, O. M., Luzzardi, P. R. G. and Cava, R. A. (2001). Introdução à visualização de informações. Revista de informática teórica e aplicada., v. 8, p. 143–158.
Fry, B. J. (2004). Computational information design. Massachusetts Institute of Technology.
Go, A., Bhayani, R. and Huang, L. (2009) “Twitter Sentiment Classification Using Distant Supervision”, Processing Cs224n Project Report, Stanford, V. 150, N. 12, P. 1–6.
Godbole, S. and Sarawagi, S. (2004) “Discriminative Methods for Multi-Labeled Classification”, Advances in Knowledge Discovery and Data, V. Lncs3056, p. 22–30.
Goldberg, A. B. and Zhu, X. (2012) “Seeing Stars When There aren’t Many Stars: Graph-Based Semi-Supervised Learning for Sentiment Categorization”, Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing.
Gu, T. and Pung, H. K. and Zhang, D. Q. (2005) “A service-oriented middleware for building context-aware services,” In: Journal of Network and Computer Applications, vol. 28, no. 1, pp. 1–18, January. [Online]. Available: DOI: 10.1016/j.jnca.2004.06.002.
Harrington, P. (2012) “Machine Learning In Action”, Manning, 2012.
Kaestner, C. A. A. (2013) “Support Vector Machines and Kernel Functions for Text Processing”, Revista de Informática Teórica E Aplicada, P. 1–7.
Kang, H., Yoo, S. J. and Han, D. (2012) “Senti-Lexicon and Improved Naïve Bayes Algorithms for Sentiment Analysis of Restaurant Reviews”, Expert Systems with Applications, V. 39, N. 5, p. 6000–6010.
Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R. and Riedl, J. (1997) “Grouplens: Applying Collaborative Filtering to Usenet News”, Communications of the Acm, V. 40, N. 3, P. 73–75.
Kontopoulos, E., Berberidis, C. and Dergiades, T. (2013) “Ontology-Based Sentiment Analysis of Twitter Posts”, Expert Systems with Applications, V. 40, N. 10, p. 4065–4074.
Lenk, A., Klems, M., Nimis, J., Tai, S., and Sandholm, T. (2009). What’s inside the Cloud? An architectural map of the Cloud landscape. In Software Engineering Challenges of Cloud Computing, 2009. CLOUD.ICSE Workshop on, volume 0 of CLOUD ’09, pages 23–31, Washington, DC, USA. IEEE.
Likert, R. (1932) “A Technique for the Measturement of Attittudes”, Archives of Psychology, V. 22, N. 140, P. 1–55.
Liu, B. (2012) “Sentiment Analysis and Opinion Mining” Morgan and Claypool Publishers, N. May.
Loke, S. (2006) “Context-Aware Pervasive Systems”, In: Auerbach Publications, Boston, Ma, USA.
Long, C., Zhang, J. and Zhut, X. (2010) “A Review Selection Approach for Accurate Feature Rating Estimation”, Proceedings of the 23rd International Conference on Computational Linguistics: Posters, N. August, p. 766–774.
Loureiro, A. A. F. and Mateus, G. R. (1998) “Introdução à Computação Móvel”, In: 11a Escola de Computação, COPPE/Sistemas, NCE/UFRJ., Brazil.
Lunardi, A. C., Viterbo, J. and Bernardini, F. C. (2015) “Um Levantamento do Uso de Algoritmos de Aprendizado Supervisionado em Mineração de Opiniões”, ENIAC - Natal, RN.
Lynn, H. D.; Radojevich-Kelley, N. (2012). Analysis of accelerator companies: An exploratory case study of their programs, processes, and early results. Small Business Institute Journal, 8:54 – 70.
Machado, M. A. S. (2013). Uma abordagem para indexacao e buscas full-text baseadas em conteu´do em sistemas de armazenamento em nuvem. Master’s thesis, Universidade Federal de Pernambuco (UFPE).
Mak, H., Koprinska, I. and Poon, J. (2003) “Intimate: A Web-Based Movie Recommender Using Text Categorization”, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003), p. 2–5.
Martineau, J. and Finin, T. (2009) “Delta tfidf : An Improved Feature Space for Sentiment Analysis”, ICWSM, May, p. 258–261.
Matsumoto, S., Takamura, H. and Okumura, M. (2005) “Sentiment Classification Using Word Sub-Sequences and Dependency Sub-Trees”, Proceedings of the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, V. 05 the 9, p. 301–311.
Mazza, R. (2009). Introduction to Information Visualization. 1. ed. Springer Publishing Company, Incorporated.
McCallum, A. and Nigam, K. (1998) “A Comparison of Event Models for Naive Bayes Text Classification”, AAAI/ICML-98 Workshop on Learning for Text Categorization, p. 41–48.
Meira, S. L. (2013). Novos Negocios Inovadores de Crescimento Empreendedor no Brasil. ISBN 978-85-77344123.
Mell, P. and Grance, T. (2009). The NIST Definition of Cloud Computing. Technical report, National Institute of Standards and Technology, Information Technology Laboratory.
Michaelis, Dicionário. (2016) ”Michaelis: Moderno Dicionário de Português Online. In: [Online]. Available: [link].
Mitchell, T. M. (1997) “Machine Learning”..
MoCATeam (2016) “Moca home page,” In: [link] (Last visited: April 2016).
Nasukawa, T. and Yi, J. (2003) “Sentiment Analysis : Capturing Favorability Using Natural Language Processing Definition of Sentiment Expressions”, 2nd International Conference on Knowledge Capture, p. 70–77.
Ortigosa, A., Martín, J. M. and Carro, R. M. (2014) “Sentiment Analysis in Facebook and its Application to e-Learning”, Computers in Human Behavior, v. 31, p. 527–541.
Pak, A. and Paroubek, P. (2010) “Twitter as a Corpus for Sentiment Analysis and Opinion Mining”, LREC, p. 1320–1326.
Paltoglou, G. and Thelwall, M. (2012) “A Study of Information Retrieval Weighting Schemes for Sentiment Analysis”, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, n. July, p. 1386–1395.
Paltoglou, G. and Thelwall, M. (2013) “Seeing Stars of Valence and Arousal in Blog Posts”, IEEE Transactions on Affective Computing, v. 4, n. 1, p. 116–123.
Pang, B. and Lee, L. (2005) “Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales”, In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (p. 115-124). Association For Computational Linguistics. v. 3, v. 1.
Pang, B. and Lee, L. (2008) “Opinion Mining And Sentiment Analysis”, Foundations and Trends in Information Retrieval, v. 2, n. 1, p. 1–135.
Pang, B., Lee, L. and Vaithyanathan, S. (2002) “Thumbs Up? Sentiment Classification Using Machine Learning Techniques”, Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing-Volume 10, n. July, p. 79–86.
Pesce, B. (2012). A menina do vale: como o empreendedorismo muda a sua vida. ISBN 978-85-7734-280-8.
Pimenta, E. (2004) “Abordagens para a Decomposiçao de Problemas Multiclasse: os Códigos de Correção de Erro e Saída”, Dissertação da Universidade do Porto, 2004.
Platt, J. C. (1998) “Fast Training of Support Vector Machines Using Sequential Minimal Optimization”, Advances in Kernel Methods, p. 185 – 208.
Pressman, R. S. (2011). Engenharia de Software: Uma Abordagem Profissional. ISBN 978-85-63308-33-7.
Prusa, J. D., Khoshgoftaar, T. M. and Dittman, D. J. (2015) “Impact of Feature Selection Techniques for Tweet Sentiment Classification”, The Twenty-Eighth International FLAIRS Conference, p. 299–304.
Qu, L.; Ifrim, G.; Weikum, G. The Bag-Of-Opinions Method For Review Rating Prediction From Sparse Text Patterns. Coling, N. August, P. 913–921, 2010.
Quinlan, J. R. (1986) “Induction of Decision Trees”, Machine Learning, v. 1, n. 1, p. 81–106.
Razzaque, Mohammad Abdur and Milojevic-Jevric, Marija and Palade, Andrei and Clarke, Siobhán. (2016). “Middleware for Internet of Things: A Survey”. In: IEEE Internet of Things Journal. Volume 3, Issue 1. [link]
Ribeiro, Artur Tavares Vilas Boas, P. G. A. O. L. M. (2015). Um fim, dois meios: Aceleradoras e incubadoras no brasil. Anais do XVI Congresso Latino-Iberoamericano de Gesta˜o da Tecnologia.
Ries, E. (2012). A Startup Enxuta. ISBN 978-85-8178-004-7.
Rodrigues, R. B., da Silva, C. M. R., Durao, F. A., Assad, R. E., Garcia, V. C., and Meira, S. R. L. (2015). A file recommendation model for cloud storage systems. In Proceedings of the Annual Conference on Brazilian Symposium on Information Systems: Information Systems: A Computer Socio-Technical Perspective - Volume 1, SBSI 2015, pages 16:111–16:118, Porto Alegre, Brazil, Brazil. Brazilian Computer Society.
Rodrigues, Ricardo Batista, R. T. A. d. O. e. R. R. d. S. (2013). Startups dirigidas a inovac¸a˜o de software: Da universidade ao mercado. Anais da III Escola Regional de Informa´ tica de Pernambuco (ERIPE), 2:162 – 169.
Rodrigues, Ricardo Batista, S. C. M. G. V. C. M. S. R. L. (2015). Craindo startups: metodos, processos e tecnicas e ferramentas. Anais do XI Simpósio Brasileiro de Sistemas de Informação, 4:13–17.
Ryan, J. and Pascoe, N. and Morse, D. (1997) “Enhanced reality fieldwork: the context-aware archaeological assistant,” In: Gaffney, V., Leusen, M. v. and Exxon, S. (Eds.).
Sá, M. P. de (2010) “Conbus: Uma plataforma de middleware de integração de sensorespara o desenvolvimento de aplicações móveis sensíveis ao contexto,” In: Dissertação de mestrado, Instituto de Informática – Universidade Federal de Goiás (INF/UFG), Brasil.
Sacramento, V. and Endler, M. and Rubinsztejn, H. K. and Lima, L. S. and Gonçalves, K. and Nascimento, F. N. and Bueno G. A. (2004) “Moca: A middleware for developing collaborative applications for mobile users” In: IEEE Distributed Systems Online, vol. 5, no. 10, p. 2,
Salber, D. and Dey, A. K. and Abowd, G. D. (1999) “The context toolkit: aiding the development of context-enabled applications,” In: CHI ’99: Proceedings of the SIGCHI conference on Human factors in computing systems. New York, NY, USA: ACM, pp. 434–441.
Salvaging the Pie (2016). [link], [accessed on Apr 23].
Schilit, B. N. and Theimer, M. M. (1994) “Disseminating active map information to mobile hosts,” In: IEEE Network, 8(5), pages 22-32. [Online]. Available: [link].
Schmidt, Albrecht and Beigl, Michael and Gellersen, Hans-W (1999). “There is more to context than location”. Computers & Graphics, Volume 23, Issue 6, Pages 893-901, ISSN 0097-8493, DOI: 10.1016/S0097-8493(99)00120-X.
Sharma, A. and Dey, S. (2012) “A Comparative Study of Feature Selection and Machine Learning Techniques for Sentiment Analysis”, RAC’S 2012, p. 1–7.
Shneiderman, B. (sep 1996). The eyes have it: a task by data type taxonomy for information visualizations. In , IEEE Symposium on Visual Languages, 1996. Proceedings.
Simpson, (primeiro) (2014). Survey Analysis: A Beginner’s Guide. DataHero Official Blog. [link], [accessed on Jul 4].
Tan, S. and Zhang, J. (2008) “An Empirical Study of Sentiment Analysis for Chinese Documents”, Expert Systems with Applications, v. 34, n. 4, p. 2622–2629.
Tanahashi, Y., Chen, C. K., Marchesin, S. and Ma, K. L. (nov 2010). An Interface Design for Future Cloud-Based Visualization Services. In 2010 IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom).
Tang, H., Tan, S. and Cheng, X. (2009) “A Survey on Sentiment Detection of Reviews”, Expert Systems with Applications, v. 36, n. 7, p. 10760–10773.
Torres, N. N. d. J. and de Souza, C. R. B. (2015). Software startup ecosystems: Initial results in the state of para. In Proceedings of the Annual Conference on Brazilian Symposium on Information Systems: Information Systems: A Computer Socio-Technical Perspective - Volume 1, SBSI 2015, pages 12:83–12:86, Porto Alegre, Brazil, Brazil. Brazilian Computer Society.
Turney, P. D. (2002) “Thumbs Up Or Thumbs Down ? Semantic Orientation Applied to Unsupervised Classification of Reviews”, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics.
Vaquero, Luis M., R.-M. L., Caceres, J., and Lindner, M. (2008). A break in the clouds: towards a cloud definition. SIGCOMM Comput. Commun. Rev., 39(1):50–55.
Vogels, W. (2008). A head in the clouds - the power of infrastructure as a service. First workshop on Cloud Computing and in Applications (CCA 2008).
Wang, H., Lu, Y. and Zhai, C. (2010) “Latent Aspect Rating Analysis on Review Text Data”, Proceedings of the 16th ACM SigKDD International Conference on Knowledge Discovery and Data Mining - KDD’10, p. 783.
Wang, T. and Wang, D. (2014) “Why Amazon’s Ratings Might Mislead You: The Story of Herding Effects”, Big Data, v. 2, n. 4, p. 196–204.
Want, R. and Hopper, A. and Falcão, V. and Gibbons, J. (1992) “The active badge location system,” In: Olivetti Research Ltd. (ORL), 24a Trumpington Street, Cambridge CB2 1QA, Tech. Rep. 92.1. [Online]. Available: [link].
Ware, C. (2004). Information visualization: perception for design. 2. ed. San Francisco, CA: Morgan Kaufman.
Weiser, M. (1991) “The computer for the twenty-first century”, In: M., Scientific American, pp. 94–100, September.
Wiebe, J. M. and Rillof, E. (2005) “Creating Subjective and Objective Sentence Classifiers from Unannotated Texts”, Computational Linguistics and Intelligent Text Processing, v. 3406, p. 486–497.
WTF Visualizations (2016). [link], [accessed on Apr 23].
Xia, R., Zong, C. and Li, S. (2011) “Ensemble of Feature Sets and Classification Algorithms for Sentiment Classification”, Information Sciences, v. 181, n. 6, p. 1138.
Zeng, W., Zhao, Y., Ou, K., and Song, W. (2009). Research on cloud storage architecture and key technologies. In Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, ICIS ’09, pages 1044–1048, New York, NY, USA. ACM.
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