Peruvian President’s Approval Rating Based on Sentiment Analysis on Tweet Data

Authors

  • Luis Fernando Solis Navarro Universidad Nacional de San Cristóbal de Huamanga

DOI:

https://doi.org/10.37467/revtechno.v11.4396

Keywords:

Natural Language Processing, Sentiment Analysis, Artificial Neural Networks, Estimated Approval of politicians

Abstract

The popular acceptance rate is a concept used to explain the increase in popular support for a political figure in a country over a given period. This figure is extracted through requested surveys that reach a certain limited sample of willing citizens and are expensive to conduct.
In this research we have implemented an automatic system for estimating the popular approval of the president of Peru using Twitter data. The method is simple, fast and highly sensitive, and can be quickly extended to other cases of opinion analysis.

References

Al Shammari, A. S. (2018). Real-time Twitter Sentiment Analysis using 3-way classifier. 21st Saudi Computer Society National Computer Conference, NCC 2018, 1–3. https://doi.org/10.1109/NCG.2018.8593205

Albawi, S., Mohammed, T. A. y Al-Zawi, S. (2017). Understanding of a convolutional neural network. International Conference on Engineering and Technology (ICET), 2017, pp. 1-6, doi: 10.1109/ ICEngTechnol.2017.8308186.

Ansari, M. Z., Aziz, M. B., Siddiqui, M. O., Mehra, H., y Singh, K. P. (2020). Analysis of Political Sentiment Orientations on Twitter. Procedia Computer Science, 167, 1821–1828. https://doi.org/10.1016/J.PROCS.2020.03.201

Balli, C., Guzel, M. S., Bostanci, E., & Mishra, A. (2022). Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing. Computational Intelligence and Neuroscience, 2022. https://doi. org/10.1155/2022/2455160

Bird, S., Klein, E. y Loper, E. (2019, 4 de septiembre). Natural language processing with Python: analyzing text with the natural language toolki. https://www.nltk.org/book/.

Cambridge University Press. (2008). Stemming and lemmatization.

Cardellino, C. (2016). Spanish Billion Words Corpus and Embeddings. https://crscardellino.ar/SBWCE/

Chambi, m. F. (2019). Análisis de opinión del microblogging twitter por la clasificación al mundial de fútbol rusia

-2018 de la selección peruana de fútbol, usando el framework spark.[tesis de pregrado, universidadnacional del antiplano]. http://repositorio.unap.edu.pe/handle/UNAP/13506

Cui, H., Lin, Y., y Utsuro, T. (2018). Sentiment Analysis of Tweets by CNN utilizing Tweets with Emoji as Training Data. Wisdom, August, 1–8. https://sentic.net/wisdom2018cui.pdf

Cuzcano, X. M., & Ayma, V. H. (2020). A comparison of classification models to detect cyberbullying in the Peruvian Spanish language on twitter. International Journal of Advanced Computer Science and Applications, 11(10), 132–138. https://doi.org/10.14569/IJACSA.2020.0111018

Canal N. (2021, October 21). Datum: Aprobación del presidente Pedro Castillo llega al 40 % | Canal N. 21 de Octubre Del 2021. https://canaln.pe/actualidad/pedro-castillo-aprobacion-mandatario-llega-al-40- segun-datum-n440163

Ferilli, S., Esposito, F., y Grieco, D. (2014). Automatic learning of linguistic resources for stopword removal and stemming from text. Procedia Computer Science, 38(C), 116–123. https://doi.org/10.1016/j. procs.2014.10.019

Gandhi, U. D., Malarvizhi Kumar, P., Chandra Babu, G., y Karthick, G. (2021). Sentiment Analysis on Twitter Data by Using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Wireless Personal Communications, 0123456789. https://doi.org/10.1007/s11277-021-08580-3

Google, L. L. C. (2005). Youtube. https://www.youtube.com/

Han, S. (2022). googletrans · PyPI. https://pypi.org/project/googletrans/

Harshith. (2019). Text Preprocessing in Natural Language Processing. Towardsdatascience. https:// towardsdatascience.com/text-preprocessing-in-natural-language-processing-using-python- 6113ff5decd8

IPSOS. (2020). Ficha Técnica: Encuesta Nacional Urbana. https://www.ipsos.com/sites/default/files/ct/news/ documents/2020-04/opinion_data_-_22_de_abril_del_2020.pdf

Khurana Batra, P., Saxena, A., Shruti, y Goel, C. (2020). Election result prediction using twitter sentiments analysis. PDGC 2020 - 2020 6th International Conference on Parallel, Distributed and Grid Computing, 182–185. https://doi.org/10.1109/PDGC50313.2020.9315789.

Kingma, D. P., y Ba, J. L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–15. https://arxiv.org/ abs/1412.6980.

Kumar, S., Morstatter, F., y Liu, H. (2013). Twitter Data Analytics. SpringerBriefs in Conputer science. https://doi. org/10.1007/978-1-4614-9372-3.

Kydros, D., & Magoulios, G. (2019). Twitter content analysis on Greek political leaders. MIBES Transactions. vol.

(1), pp. 30–44.

Leonard Richardson. (2020). Beautiful Soup Documentation. https://www.crummy.com/software/BeautifulSoup/ bs4/doc/

Liu, Z., Lin, Y., & Sun, M. (2020). Representation Learning and NLP. Representation Learning for Natural Language Processing, 1–11. https://doi.org/10.1007/978-981-15-5573-2_1

Maharani, W., & Effendy, V. (2022). Big five personality prediction based in Indonesian tweets using machine learning methods. International Journal of Electrical and Computer Engineering, 12(2), 1973–1981. https://doi.org/10.11591/ijece.v12i2.pp1973-1981

Medianero Burga, D. (2014). Metodología de Estudios de Línea de Base. Pensamiento Crítico, 15, 061. https://doi. org/10.15381/pc.v15i0.8994

Meta Inc. (2004). Facebook. https://www.facebook.com/

Mohammad, S. A. I. F. M. M., Urney, P. E. D. T., y Canada, C. (2012). CROWDSOURCING A WORD – EMOTION

ASSOCIATION LEXICON. Computational Intelligence. https://onlinelibrary.wiley.com/doi/10.1111/ j.1467-8640.2012.00460.x

Mongodb. (2021). What Is Unstructured Data? | MongoDB. https://www.mongodb.com/unstructured-data Monhaler, Edna Maria; Matias Miranda, A. F. (2017). La diversidad lingüística del español en el mundo

contemporáneo: propuestas de actividades didácticas. En Actas Del III Congreso Internacional SICELE. Investigación e Innovación En ELE. Evaluación y Variedad Lingüística Del Español. https://cvc.cervantes. es/ensenanza/biblioteca_ele/sicele/sicele03/006_matiasmonheler.htm

Parmezan, A. R. S., Souza, V. M. A., y Batista, G. E. A. P. A. (2019). Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information Sciences, 484, 302–337. https://doi.org/10.1016/j.ins.2019.01.076

Paul Davison, R. S. (2020). Clubhouse. https://www.clubhouse.com/

Pennington, J., Socher, R., y Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. https://nlp. stanford.edu/pubs/glove.pdf

Poornima, A., Nataraj, N., Nithya, R., Nirmala, D., y Divya, P. (2022). Sentiment Analysis of Tweets in Twitter Using CNN. 2022 International Conference on Computer Communication and Informatics, ICCCI 2022, 25–28. https://doi.org/10.1109/ICCCI54379.2022.9740779

Poria, S., Hussain, A., y Cambria, E. (2018). Multimodal Sentiment Analysis (Vol. 8). Springer International Publishing. https://doi.org/10.1007/978-3-319-95020-4

Prastyo, P. H., Sumi, A. S., Dian, A. W., & Permanasari, A. E. (2020). Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel. Journal of Information Systems Engineering and Business Intelligence, 6(2), 112. https://doi.org/10.20473/ jisebi.6.2.112-122

Rai, A., & Borah, S. (2021). Study of Various Methods for Tokenization. Lecture Notes in Networks and Systems, 137, 193–200. https://doi.org/10.1007/978-981-15-6198-6_18

Rodríguez, C. G. and Tule, L. G. (2019). Honduras 2019: Persistent economic and social instability and institutional weakness. Revista de Ciencia Politica, 40, 379–400. https://www.scielo.cl/scielo.php?script=sci_ arttext&pid=S0718-090X2020005000112&lng=en&nrm=iso&tlng=en

Ross Ihaka, R. G. (1993). R: The R Project for Statistical Computing. https://www.r-project.org/

Shaghaghi, N., Calle, A. M., Manuel Zuluaga Fernandez, J., Hussain, M., Kamdar, Y., & Ghosh, S. (2021). Twitter Sentiment Analysis and Political Approval Ratings for Situational Awareness. Proceedings - 2021 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2021, 59–65. https://doi.org/10.1109/COGSIMA51574.2021.9475935

Sharma, A., & Ghose, U. (2020). Sentimental Analysis of Twitter Data with respect to General Elections in India.

Procedia Computer Science, 173(2019), 325–334. https://doi.org/10.1016/j.procs.2020.06.038

Silva, H., Andrade, E., Araujo, D., & Dantas, J. (2022). Sentiment Analysis of Tweets Related to SUS before and during COVID-19 pandemic. IEEE Latin America Transactions, 20(1), 6–13. https://doi.org/10.1109/ TLA.2022.9662168

Statista. (2021). Media usage in an internet minute as of August 2021. Statista; Springer Vienna. https://doi. org/10.1007/s13278-021-00853-w

Twitter. (2006). Twitter. https://twitter.com/

Downloads

Published

2022-12-28

How to Cite

Solis Navarro, L. F. (2022). Peruvian President’s Approval Rating Based on Sentiment Analysis on Tweet Data. TECHNO REVIEW. International Technology, Science and Society Review Revista Internacional De Tecnología, Ciencia Y Sociedad, 12(1), 1–13. https://doi.org/10.37467/revtechno.v11.4396

Issue

Vol. 12 No. 1 (2022): Monograph: "What is new in Social Networking 2.0, 3.0 and 4.0?"

Section

Research Articles (Special Issue)

License

Those authors who publish in this journal accept the following terms:

  1. Authors will keep the moral right of the work and they will transfer the commercial rights.
  2. After 1 year from publication, the work shall thereafter be open access online on our website, but will retain copyright.
  3. In the event that the authors wish to assign an Creative Commons (CC) license, they may request it by writing to publishing@eagora.org