dc.contributor | Amaya, Sindy Paola | |
dc.contributor | https://orcid.org/0000-0002-1714-1593 | |
dc.contributor | https://scholar.google.es/citations?user=Gg2sofAAAAAJ&hl=es | |
dc.contributor | https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000796425 | |
dc.contributor | Universidad Santo Tomás | |
dc.creator | Granados Figueroa, Juan David | |
dc.date.accessioned | 2021-07-15T20:12:06Z | |
dc.date.available | 2021-07-15T20:12:06Z | |
dc.date.created | 2021-07-15T20:12:06Z | |
dc.date.issued | 2021-03-01 | |
dc.identifier | Granados, Figueroa, J. D. (2021). Aplicación de técnicas de Machine Learning para hacer análisis de polaridad de sentimientos en texto para detectar tendencias de opinión en plataformas online. [Trabajo de pregrado, Universidad Santo Tomás]. Repositorio Institucional. | |
dc.identifier | http://hdl.handle.net/11634/34933 | |
dc.identifier | reponame:Repositorio Institucional Universidad Santo Tomás | |
dc.identifier | instname:Universidad Santo Tomás | |
dc.identifier | repourl:https://repository.usta.edu.co | |
dc.description.abstract | The Internet has allowed millions of people to connect and generate interactions, as in social networks, which has generated a lot of unstructured information, which is difficult for a group of human beings to analyze, due to its large amount. In this work, Machine Learning techniques are applied to analyze the polarity of sentiment in Spanish language, of the comments of Twitter users about various topics. Sentiment polarity analysis allows you to analyze opinion trends quickly and automatically, allowing companies and organizations to have valuable information for decision-making. Recurrent Neural Networks are implemented, which are one of the methods that show the best results for sequence analysis, through the application of Deep Learning, which belongs to the field of Machine Learning and which, in addition, avoids the need to perform extraction of characteristics, which would require careful selection by language experts. Keras is used to program the model with tensorflow, and accuracy results are obtained very close to the most advanced systems in the state of the art. The model is trained with a Dataset of 49,444 sentences labeled with positive or negative, based on the TASS corpus | |
dc.language | spa | |
dc.publisher | Universidad Santo Tomás | |
dc.publisher | Pregrado Ingeniería Electrónica | |
dc.publisher | Facultad de Ingeniería Electrónica | |
dc.relation | [1] V. Kharde y P. S. Sonawane, «Sentiment Analysis of Twitter Data: A Survey of Techniques,» International Journal of Computer Aplications, vol. 139(11), pp. 5-15, 2016. | |
dc.relation | [2] M. Atique y . H. P. Patil , «Sentiment Analysis for Social Media: A Survey,» de 2nd International Conference on Information Science and Security (ICISS), Seoul, 2015. | |
dc.relation | [3] Y. LeCun, Y. Bengio y G. Hinton, «Deep Learning,» NATURE, vol. 521, pp. 436-444, 2015. | |
dc.relation | [4] I. Goodfellow, Y. Bengio y A. Courville, Deep Learning, MIT Press, 2016. | |
dc.relation | [5] J. Chung, C. Gulcehre, K. Cho y Y. Bengio, «Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,» de NIPS Deep Learning and Representation Learning Workshop, 2014. | |
dc.relation | [6] E. Martínez-Cámara y J. Villena-Román, «TASS - Workshop on Sentiment Analysis at SEPLN,» Procesamiento del Lenguaje Natural, vol. 50, pp. 37-44, 2013. | |
dc.relation | [7] L. Rosenberg y N. Pescetelli , «Artificial Swarm Intelligence Amplifies Accuracy when Predicting Financial Markets,» de IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York City, 2017. | |
dc.relation | [8] S. Al-Saqqa, H. Abdel-Nabi y A. Awajan, «A Survey of Textual Emotion Detection,» de 2018 8th International Conference on Computer Science and Information Technology (CSIT), Amman, Jordan, 2018. | |
dc.relation | [9] H. S. Kisan, H. A. Kisan y A. P. Suresh , «Collective intelligence & sentimental analysis of twitter data by using StandfordNLP libraries with software as a service (SaaS),» de IEEE international Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, 2016. | |
dc.relation | [10] K. Zvarevashe y O. O. Olugbara , «A framework for sentiment analysis with opinion mining of hotel reviews,» de 2018 Conference on Information Communications Technology and Society (ICTAS), Durban, South Africa, 2018. | |
dc.relation | [11] A. Salinca, «Business Reviews Classification Using Sentiment Analysis,» de 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania, 2015. | |
dc.relation | [12] the sckit-learn comunity, «sckit-learn,» 2010. [En línea]. Available: https://scikit-learn.org/stable/index.html. [Último acceso: 10 May 2019]. | |
dc.relation | [13] H. Kaur, V. Mangat y Nidhi , «A survey of sentiment analysis techniques,» de International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2017. | |
dc.relation | [14] A. Patel y K. T. Arvind , «Sentiment Analysis by using Recurrent Neural Network,» de 2° Internacional conference on advanced computing software engineering (ICACSE-2019), 2019. | |
dc.relation | [15] «IMBD Datasets,» [En línea]. Available: https://www.imdb.com/interfaces/. [Último acceso: 10 May 2020]. | |
dc.relation | [16] M. A. Paredes-Valverde, R. Colomo-Palacios, M. d. P. Salas-Zárate y R. Valencia-García, «Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach,» Hindawi, Scientific Programming, vol. Volume 2017, 26 Oct 2017. | |
dc.relation | [17] A. Radford, R. Jozefowicz y I. Sutskever, «Learning to Generate Reviews and Discovering Sentiment,» arVix preprint arXiv:1704.01444v2 [cs.LG], 6 Apr 2017. | |
dc.relation | [18] J. ̃. Paulo Aires, C. Padilha, C. Quevedo y F. Meneguzzi, «A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets,» de International Joint Conference on Neuronal Networks (IJCNN), Rio de Janeiro, Brazil, 2018. | |
dc.relation | [19] S. Chen, C. Peng y L. Cai, «A Deep Neural Network Model for Target-based Sentiment Analysis,» de International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018. | |
dc.relation | [20] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, G. Aidan N. , L. Kaiser y I. Polosukhin, «Attention Is All You Need,» arXiv preprint arXiv:1706.03762v5 [cs.CL], 6 Dec 2017. | |
dc.relation | [21] «BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,» arVix preprint arXiv:1810.04805v2 [cs.CL], 24 May 2019. | |
dc.relation | [22] Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov y Q. V. Le, «XLNet: Generalized Autoregressive Pretraining for Language Understanding,» arVix preprinted arXiv:1906.08237v2 [cs.CL], 2 Jan 2020. | |
dc.relation | [23] T. B. Brown∗, B. Mann, N. Ryder∗ y M. Subbiah∗, «Language Models are Few-Shot Learners,» arVix preprint arXiv:2005.14165v4 [cs.CL], 22 Jul 2020. | |
dc.relation | [24] L. You y B. Tuncer, «Exploring public sentiments for livable places based on a crowdcalibrated sentimet analysis mechanism,» de IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, 2016. | |
dc.relation | [25] Q. Song, S. Almahdi y S. Y. Yang, «Entropy based measure sentiment analysis in the financial market,» de IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, Hl, 2017. | |
dc.relation | [26] C. W. Park y D. R. Seo, «Sentiment analysis of Twitter corpus related to artificial intelligence assistants,» de 5th International Conference on Industrial Engineering and Applications (ICIEA), Singapore, 2018. | |
dc.relation | [27] X. Yu, Y. Liu , X. Huang y A. An, «Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain,» vol. 24, nº 4, pp. 720 - 734, 2010. | |
dc.relation | [28] N. Buduma, Fundamentals of Deep Learning, First Edition ed., Sebastopol., CA 95472.: O'Reilly Media, Inc., 2017, pp. 1-37. | |
dc.relation | [29] Y. Bengio, A. Courville y P. Vincent, «Representation Learning: A Review and New Perspectives,» IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, nº 8, pp. 1798-1828, Agosto 2013. | |
dc.relation | [30] J. Schmidhuber, «Deep learning in neural networks: An overview,» Neural networks, vol. 61, pp. 85-117, 2015. | |
dc.relation | [31] A. Graves, Supervised Sequence Labelling with Recurrent Neural Networks, vol. 385, New York: Springe, 2012. | |
dc.relation | [32] F. Provost, T. Fawcett y R. Kohavi, «The Case against Accuracy Estimation for Comparing Induction Algorithms,» de Fiftteen Internacional Conference on Machine Learning (IMLC), Madison, Wi, 1998. | |
dc.relation | [33] N. Chinchor, Ph.D, «MUC-4 EVALUATION METRICS,» de Fourth Message Uunderstanding Conference (MUC-4): Proceedings of a Conference Held in McLean, Virginia, June 16-18, 1992. | |
dc.relation | [34] «scikit-learn, k-fold cross validation,» [En línea]. Available: http://scikit-learn.sourceforge.net/stable/modules/generated/sklearn.cross_validation.KFold.html. [Último acceso: 15 Jul 2020]. | |
dc.relation | [35] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever y R. Salakhutdinov, «Dropout: A Simple Way to Prevent Neural Networks from,» Journal of Machine Learning Research, vol. 15, pp. 1929-1958, 2014. | |
dc.relation | [36] Google, «tensorflow,» [En línea]. Available: https://www.tensorflow.org/guide/. [Último acceso: 17 Abril 2020]. | |
dc.relation | [37] F. Chollet, «keras,» [En línea]. Available: https://keras.io/. [Último acceso: 17 04 2020]. | |
dc.relation | [38] «https://www.python.org/,» [En línea]. [Último acceso: 15 Feb 2020]. | |
dc.relation | [39] Google, «tensorflow keras,» [En línea]. Available: https://www.tensorflow.org/guide/keras/overview. [Último acceso: 10 Abril 2020]. | |
dc.relation | [40] Google, «Google Colaboratory,» 25 May 2020. [En línea]. Available: https://colab.research.google.com/notebooks/intro.ipynb. | |
dc.rights | Abierto (Texto Completo) | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | http://purl.org/coar/access_right/c_abf2 | |
dc.title | Aplicación de técnicas de Machine Learning para hacer análisis de polaridad de sentimientos en texto para detectar tendencias de opinión en plataformas online | |