Analysis of Classification Algorithms for Emotion Detection in Brazilian Portuguese Tweets
DOI:
https://doi.org/10.5753/isys.2019.600Keywords:
Data mining, Data science, Information SystemsAbstract
With increasing access to the Web, large amounts of content are produced daily. The study of such contents allows the discovery of new knowledge. In this sense, this work presents an analysis of algorithms that allow the detection of emotions in tweets in the Brazilian Portuguese language. Thus, ten algorithms are considered, from decision trees to classifiers based on Bayes model, addressing altogether, seven classes of emotions: sad, upset, love, happy, anger, envy and irony. The results of the experimental evaluation are better when classifying relationships of distinct emotions, reaching 85% accuracy with a Naive Bayes algorithm. On the other hand, relations between close feelings present results inferior to 70% of correctness in some cases. Moreover, Naive Bayesbased classification algorithms present efficient results in a variety of contexts, in addition to having consistent language-independent behavior.
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References
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Bouazizi, M. and Ohtsuki, T. (2016). Sentiment analysis in twitter: From classification to quantification of sentiments within tweets. In 2016 IEEE Global Communications Conference (GLOBECOM), pages 1–6.
Caetano, J. A., Lima, H. S., dos Santos, M. F., and Marques-Neto, H. T. (2017). Utilizando análise de sentimentos para definição da homofilia política dos usuários do twitter durante a eleição presidencial americana de 2016. In Procs. of the VI Brazilian Workshop on Social Network Analysis and Mining, pages 480–491, São Paulo, Brasil.
Dwivedi, S., Kasliwal, P., and Soni, S. (2016). Comprehensive study of data analytics tools (rapidminer, weka, r tool, knime). In Procs. of Symposium on Colossal Data Analysis and Networking (CDAN), pages 1–8. IEEE.
França, T. C. d. and Oliveira, J. (2014). Análise de sentimento de tweets relacionados aos protestos que ocorreram no brasil entre junho e agosto de 2013. In of Brazilian Workshop on Social Network Analysis and Mining (BraSNAM), pages 128–139. IEEE.
Garg, T. and Khurana, S. S. (2014). Comparison of classification techniques for intrusion detection dataset using weka. In Recent Advances and Innovations in Engineering (ICRAIE), 2014, pages 1–5. IEEE.
Garimella, K., Morales, G. D. F., Gionis, A., and Mathioudakis, M. (2018). Quantifying controversy on social media. ACM Transactions on Social Computing, 1(1):3.
LaFree, G. (1999). Declining violent crime rates in the 1990s: Predicting crime booms and busts. Annual Review of Sociology, 25(1):145–168.
Matsuda, Y. (2017). Development of emotion teaching interfaces using emoticons and emojis. In 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), volume 2, pages 253–258.
Moraes, R. L. and Sampaio, J. O. (2017). Um método de indexação e organização de informações baseado em um sociograma–aplicação em domínios específicos. iSys-Revista Brasileira de Sistemas de Informação, 10(2):48–84.
Neri, F., Aliprandi, C., Capeci, F., Cuadros, M., and By, T. (2012). Sentiment analysis on social media. In Advances in Social Networks Analysis and Mining (ASONAM), 2012
IEEE/ACM International Conference on, pages 919–926. IEEE.
Pandey, A. K. and Rajpoot, D. S. (2016). A comparative study of classification techniques by utilizing weka. In Procs. of International Conference on Signal Processing and Communication (ICSC), pages 219–224. IEEE.
Rout, J. K., Choo, K.-K. R., Dash, A. K., Bakshi, S., Jena, S. K., and Williams, K. L. (2018). A model for sentiment and emotion analysis of unstructured social media text. Electronic Commerce Research, 18(1):181–199.
Sharma, N., Pabreja, R., Yaqub, U., Atluri, V., Chun, S. A., and Vaidya, J. (2018). Web-based application for sentiment analysis of live tweets. In Procs. of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, pages 120:1–120:2, Delft, The Netherlands.
Witten, I. H., Frank, E., and Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Series in Data Management Systems.
Morgan Kaufmann. Yadollahi, A., Shahraki, A. G., and Zaiane, O. R. (2017). Current state of text sentiment analysis from opinion to emotion mining. ACM Computing Surveys (CSUR), 50(2):25.
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Published
2019-09-18
How to Cite
Kansaon, D. P., Brandão, M. A., & Pinto, S. A. de P. (2019). Analysis of Classification Algorithms for Emotion Detection in Brazilian Portuguese Tweets. ISys - Brazilian Journal of Information Systems, 12(3), 116–138. https://doi.org/10.5753/isys.2019.600
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