Abstract
In recent years, the machine-learning field, deep neural networks has been an important topic of research, used in several disciplines such as pattern recognition, information retrieval, classification and natural language processing. Is in the last that this paper it’s going to be our principal topic, in this branch exist an specific task that in literature is called Sentiment Analysis were the principal function is to detect if an opinion is positive or negative.
In the paper we show how use this subset of the machine learning knowledge and use it for give us an insight in the question: what is the perception in a business or a product by means of the opinion of the consumers in social networks?
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References
Gautam, G., Yadav, D.: Sentiment Analysis of Twitter Data Using Machine Learning Approaches and Semantic Analysis, Department of Computer Science and Engineering Jaypee Institute of Information technology Noida, India (2014)
Maharani, W.: Microblogging Sentiment Analysis with Lexical Based and Machine Learning Approaches, Faculty of Informatics Telkom Institute of Technology, Bandung Indonesia (2013)
Waila, P., Marisha, R., Singh, V.K., Singh, M.K.: Evaluating Machine Learning and Unsupervised Semantic Orientation Approaches for Sentiment Analysis of Textual Reviews, Banaras Hindu University, Varanasi India (2012)
Zweig, G., Mikolov, T.: Context Dependent Recurrent Neural Network Language Model. Microsoft, Redmond (2012)
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3(6), 1137–1155 (2003)
Schwenk, H.: Continuous space language models. Comput. Speech Lang. 21(3), 492–518 (2007)
Mnih, A., Hinton, G.: Three new graphical models for statistical language modelling. In: Proceedings of the 24th International Conference on Machine Learning (2007)
Le, H.S., Oparin, I., Allauzen, A., Gauvain, J.-L., Yvon, F.: Structured output layer neural network language model. In: ICASSP (2011)
Hai Son, L., Allauzen, A., Yvon, F.: Measuring the influence of long range dependencies with neural network language models. In: Proceedings of the Workshop on the Future of Language Modeling for HLT (NAACL/HLT 2012) (2012)
Mikolov, T., Karafiat, M., Cernocky, J., Khudanpur, S.: Recurrent neural network based language model. In: Proceedings of Interspeech 2010 (2010)
Hinton, G.E.: Learning distributed representations of concepts. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, MA (1986)
Elman, J.L.: Distributed representations, simple recurrent networks, and grammatical structure. Mach. Learn. 7(2), 195–225 (1991)
Le, Q., Mikolov, T.: 31st International Conference on Machine Learning, Beijing, China (2014)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.L., CORPORATE PDP Research Group (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 318–362. MIT Press, Cambridge (1986)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv preprint arXiv:1301.3781
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
Mikolov, T., Yih, S.W., Zweig, G.: Linguistic regularities in continuous space word representations. In: NAACL HLT (2013)
Morin, F., Bengio, Y.: Hierarchical probabilistic neural network language model. In: Proceedings of the International Workshop on Artificial Intelligence and Statistics, pp. 246–252 (2005)
Natural Language Processing APIs and Python NLTK Demos, 23 March 2015. http://text-processing.com/demo/sentiment/5
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Vargas, M.P., Parra, O.J.S., Rico, M.J.E. (2017). Business Perception Based on Sentiment Analysis Through Deep Neuronal Networks for Natural Language Processing. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. ruSMART NsCC NEW2AN 2017 2017 2017. Lecture Notes in Computer Science(), vol 10531. Springer, Cham. https://doi.org/10.1007/978-3-319-67380-6_33
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DOI: https://doi.org/10.1007/978-3-319-67380-6_33
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