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Using Supervised Classification to Detect Political Tweets with Political Content

Published:16 October 2018Publication History

ABSTRACT

Social media platforms have been increasingly used by modern society. In most platforms, users usually share content on various subjects and, in particular, politics is a favorite one. There are many interests in detecting and analyzing such a political content. However, there is a challenge in the process of detecting specific subjects from social media data mainly due to its informality. In this paper, we propose and compare two techniques, based on supervised classification, for the detection of tweets with political content. The results obtained by our approach have demonstrated satisfactory performance, which motivates further research to be undertaken.

References

  1. Abebe Abeshu and Naveen Chilamkurti. 2018. Deep learning: the frontier for distributed attack detection in Fog-to-Things computing. IEEE Communications Magazine 56, 2 (2018), 169--175. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Charu C. Aggarwal and ChengXiang Zhai. 2012. A Survey of Text Clustering Algorithms. Springer US, Boston, MA, 77--128.Google ScholarGoogle Scholar
  3. Liliya Akhtyamova, John Cardiff, and Andrey Ignatov. 2017. Twitter Author Profiling Using Word Embeddings and Logistic Regression. Proceedings of Conference and Labs of the Evaluation Forum - CLEF 2017.Google ScholarGoogle Scholar
  4. Ika Alfina, Dinda Sigmawaty, Fitriasari Nurhidayati, and Achmad Nizar Hidayanto. 2017. Utilizing Hashtags for Sentiment Analysis of Tweets in The Political Domain. In Proceedings of the 9th International Conference on Machine Learning and Computing. ACM, 43--47. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Andre Luiz Firmino Alves, Claudio De Souza Baptista, Anderson Almeida Firmino, Maxwell Guimaraes De Oliveira, and Anselmo Cardoso De Paiva. 2014. A Comparison of SVM Versus Naive-Bayes Techniques for Sentiment Analysis in Tweets. In Proceedings of the 20th Brazilian Symposium on Multimedia and the Web - WebMedia 14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Rafael T. Anchieta and Raimundo S. Moura. 2017. Exploring Unsupervised Learning Towards Extractive Summarization of User Reviews. In Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web - WebMedia 17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Matheus Araujo, Julio Reis, Adriano Pereira, and Fabricio Benevenuto. 2016. An evaluation of machine translation for multilingual sentence-level sentiment analysis. In Proceedings of the 31st Annual ACM Symposium on Applied Computing - SAC 16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Farzindar Atefeh and Wael Khreich. 2015. A survey of techniques for event detection in twitter. Computational Intelligence 31, 1 (2015), 132--164. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Ricardo Baeza-Yates, Berthier Ribeiro-Neto, et al. 1999. Modern information retrieval. Vol. 463. ACM press New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016).Google ScholarGoogle Scholar
  11. Alberto Carreras Mesa, Mari Carmen Aguayo-Torres, Francisco J. Martin-Vega, Gerardo Gómez, Francisco Blanquez-Casado, Isabel M. Delgado-Luque, and Jose Entrambasaguas. 2018. Link abstraction models for multicarrier systems: A logistic regression approach. International Journal of Communication Systems 31, 1 (2018).Google ScholarGoogle Scholar
  12. Moon-tong Chan, Dalei Yu, and Kelvin K. W. Yau. 2015. Multilevel cumulative logistic regression model with random effects: Application to British social attitudes panel survey data. Computational Statistics & Data Analysis 88 (2015), 173--186. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Eric Fernandes de Mello Araújo and Dave Ebbelaar. 2018. Detecting Dutch political tweets: A classifier based on voting system using supervised learning. In 10th International Conference on Agents and Artificial Intelligence, ICAART 2018. SciTePress.Google ScholarGoogle ScholarCross RefCross Ref
  14. Maite Giménez, Tomás Baviera, Germán Llorca, José Gámir, Dafne Calvo, Paolo Rosso, and Francisco Rangel. 2017. Overview of the 1st classification of spanish election tweets task at ibereval 2017. In Notebook Papers of 2nd SEPLN Workshop on Evaluation of Human Language Technologies for Iberian Languages (IBEREVAL), Murcia, Spain, September, Vol. 19.Google ScholarGoogle Scholar
  15. Frank E. Harrell. 2001. Regression modeling strategies, with applications to linear models, survival analysis and logistic regression. In Springer Series in Statistics. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Abdalraouf Hassan and Ausif Mahmood. 2018. Convolutional Recurrent Deep Learning Model for Sentence Classification. IEEE Access 6 (2018), 13949--13957.Google ScholarGoogle ScholarCross RefCross Ref
  17. Marti Hearst. 2003. What is text mining. SIMS, UC Berkeley (2003).Google ScholarGoogle Scholar
  18. Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2016. Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016).Google ScholarGoogle Scholar
  19. Dan Jurafsky and James H. Martin. 2009. Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. In Prentice Hall series in artificial intelligence. Prentice Hall, Pearson Education International, 1--1024. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Ankush Khandelwal, Sahil Swami, Syed Sarfaraz Akhtar, and Manish Shrivastava. 2017. Classification Of Spanish Election Tweets (COSET) 2017: Classifying Tweets Using Character and Word Level Features. In IberEval@ SEPLN. 49--54.Google ScholarGoogle Scholar
  21. Yuancheng Li, Rong Ma, and Runhai Jiao. 2015. A hybrid malicious code detection method based on Deep Learning. International Journal of Software Engineering and its Applications 9, 5 (2015), 205--216.Google ScholarGoogle Scholar
  22. George Loukas, Tuan Vuong, Ryan Heartfield, Georgia Sakellari, Yongpil Yoon, and Diane Gan. 2018. Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning. IEEE Access 6 (2018), 3491--3508.Google ScholarGoogle ScholarCross RefCross Ref
  23. Kevin P. Murphy. 2012. Machine Learning: A Probabilistic Perspective. Adaptive Computation and Machine Learning. In Adaptive Computation and Machine Learning series. MIT press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Arman Khadjeh Nassirtoussi, Saeed Aghabozorgi, Teh Ying Wah, and David Chek Ling Ngo. 2015. Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment. Expert Systems with Applications 42, 1 (2015), 306--324. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Nnamdi I. Nwulu. 2017. Evaluation of machine learning classification algorithms & missing data imputation techniques. In International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 1--5.Google ScholarGoogle ScholarCross RefCross Ref
  26. Erik Tjong Kim Sang, Herbert Kruitbosch, Marcel Broersma, and Marc Esteve Del Valle. 2017. Determining the function of political tweets. In IEEE 13th International Conference on e-Science. IEEE, 438--439.Google ScholarGoogle ScholarCross RefCross Ref
  27. Sandro Skansi. 2018. Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Karen Sparck Jones. 1972. A statistical interpretation of term specificity and its application in retrieval. Journal of documentation 28, 1 (1972), 11--21.Google ScholarGoogle ScholarCross RefCross Ref
  29. Hastie Trevor, Robert Tibshirani, and Jerome H. Friedman. 2009. The elements of statistical learning: Data Mining, Inference, and Prediction. New York, NY: Springer.Google ScholarGoogle Scholar
  30. David Watts, K. M. George, T. K. Ashwin Kumar, and Zenia Arora. 2016. Tweet sentiment as proxy for political campaign momentum. In IEEE International Conference on Big Data. IEEE, 2475--2484.Google ScholarGoogle ScholarCross RefCross Ref
  31. Xiang Zhu, Yuanping Nie, Songchang Jin, Aiping Li, and Yan Jia. 2015. Spammer detection on online social networks based on logistic regression. In International Conference on Web-Age Information Management. Springer, 29--40.Google ScholarGoogle ScholarCross RefCross Ref

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      • Published in

        cover image ACM Other conferences
        WebMedia '18: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web
        October 2018
        437 pages
        ISBN:9781450358675
        DOI:10.1145/3243082

        Copyright © 2018 ACM

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        Publication History

        • Published: 16 October 2018

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        WebMedia '18 Paper Acceptance Rate37of111submissions,33%Overall Acceptance Rate270of873submissions,31%

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