Abstract
In this paper we deploy a deep architecture for convolutional neural networks for understanding of Croatian. We follow the same approach as in [34], and we use deep learning on character inputs on a sentiment analysis dataset in Croatian. Although we have archived considerable results (without any complex parsing or background knowledge), the result was inferior to that reported in the abovementioned paper. As Croatian is one of the low-resource languages, there are considerable links between using such an approach (that maximizes the role of data) and sustainability. The main objective of this chapter is to give a clear understanding of the position of low-resource languages and propose a direction for sustainable development of language technologies illustrated using convolutional neural networks for textual sentiment analysis . Impact of this research to scientific but also business community is significant due to fact that every method with acceptable ratio of simplicity and effectiveness can be included inside more complex logic environment focusing on specific language or appliance in specific area. Since language structure is generally not easy to manage, there is constant need for improvement of tools used to score text data especially while majority of unstructured data analysis tools often transfer various data to text and create further analysis paths form there.
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Notes
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In the following we shall use the abbreviation NLP to stand for “natural language processing”.
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References
A. Abbasi, H. Chen, A. Salem, sentiment analysis in multiple languages: feature selection for opinion classification in web forums. ACM Trans. Inf. Syst. (TOIS) 26(3) (2008)
M. Abhijit, K. Diptesh, N. Seema N., Kuntal, B. Pushpak, Leveraging cognitive features for sentiment analysis, in Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning (2016)
D.H. Ackley, G.E. Hinton, T.J. Sejnowski, A learning algorithm for boltzmann machines. Cogn. Sci. 9, 147–169 (1985)
A. Anton, J. Earp, Q. He, D. Bolchini Stufflebeam, C. Jensen, Financial privacy policies and the need for standardization. Secur. Priv. IEEE 2(2), 36–45 (2004)
C. Banea, R. Mihalcea, J. Wiebe, S. Hassan, Multilingual subjectivity analysis using machine translation, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2008) (2008)
P. Blackburn, J. Bos, Representation and Inference for Natural Language: A First Course in Computational Semantics (Center for the Study of Language and Information, Amsterdam, 2005)
B. Dropuljic, S. Skansi, R. Kopal, Analyzing affective elements using acoustic and linguistic features, in Proceedings of the Central European Conference on Information and Intelligent Systems (CECIIS 2016) (2016), pp. 201–206
B. Franks, Timing the Big Data Tidal Wave: Finding Opportunities in Huge Dana Streams with Advanced Analytics (Wiley and SAS Business Series, New York, 2012)
A. Frome, G. Corrado, J. Shlens, J. Dean, T. Mikolov, Devise: A deep visual-semantic embedding model, in Advances in Neural Information Processing Systems (2013), pp. 2121–2129
D.J. Gunkel, A vindication of the rights of machines. Philos. Technol. 27(1), 113–132 (2014)
J. Haiman, Talk Is Cheap: Sarcasm, Alienation, and the Evolution of Language (Oxford University Press, Oxford, 1998)
M. Hassoun, Fundamentals of Artificial Neural Networks (MIT Press, Cambridge, 2003)
D. Hebb, The Organization of Behavior (Wiley, New York, 1949)
G.E. Hinton, Learning distributed representations of concepts, in Proceedings of the Ninth Conference of the Cognitive Science Society, Amherst, MA (1986), pp. 1–12
J.R. Kalyvas, M.R. Overly, Big Data: A Business and Legal Guide (Taylor and Francis Group, New York, 2015)
Q. Le, T. Mikolov, Distributed Representations of Sentences (2014). arXiv:1405.4053
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Y. LeCun, Une procédure d’apprentissage pour réseau a seuil asymmetrique. Proc. Cogn. 85, 599–604 (1985)
V. Lyovin, An Introduction to the Languages of the World (Oxford University Press, New York, 1997)
F. Magdoff, J.B. Foster, What Every Environmentalist Needs to Know About Capitalism: A Citizen’s Guide to Capitalism and the Environment (Monthly Review Press, New York, 2011)
W. McCulloch, W. Pitts, A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)
M. Minsky, S. Papert, An Introduction to Computational Geometry (MIT Press, Cambridge, 1969)
D.B. Parker, Learning-logic. Technical report-47, MIT Center for Computational Economics, Cambridge (1985)
F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386–408 (1958)
I. Rubenstein, Regulating privacy by design. Berkeley Technol. Law J. 26, 1409 (2012)
S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach (Pearsons, Harlow, 2009)
R. Schiller, Irrational Exhuberance (Princeton University Press, New York, 2000)
V.M. Schonberger, K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work and Think (Houghton Mifflin Harcourt Publishing Company, New York, 2013)
P. Singer, Animal Liberation: A New Ethics for Our Treatment of Animals (New York Review of Books, New York, 1975)
M.W. Vail, J.B. Earp, A.I. Anton, An empirical study of consumer perceptions and comprehension of web-site privacy policies. IEEE Trans. Eng. Manage. 55(3), 442–454 (2008)
P.J. Werbos, Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences (Harvard University, Cambridge, 1975)
T. Wilson, J. Wiebe, P. Hoffman, Recognizing contextual polarity in phrase-level sentiment analysis, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2005) (2005)
J.D. Young, Commitment analysis to operationalize software requirements from privacy notices. Requirement Eng. 16(1), 33–46 (2010)
X. Zhang, Y. LeCun, Text Understanding from Scratch (2016). arXiv:1502.01710
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Mršić, L., Kopal, R., Klepac, G. (2017). Analyzing Slavic Textual Sentiment Using Deep Convolutional Neural Networks. In: Sangaiah, A., Abraham, A., Siarry, P., Sheng, M. (eds) Intelligent Decision Support Systems for Sustainable Computing. Studies in Computational Intelligence, vol 705. Springer, Cham. https://doi.org/10.1007/978-3-319-53153-3_11
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