Abstract
Nowadays, a huge amount of data is generated daily around the world and many machine learning tasks require labeled data, which sometimes is not available. Manual labeling such amount of data may consume a lot of time and resources. One way to overcome this limitation is to learn from both labeled and unlabeled data, which is known as semi-supervised learning. In this paper, we use a positive-unlabeled (PU) learning technique called Random Walk in Feature-Sample Networks (RWFSN) to perform semi-supervised sentiment analysis, which is an important machine learning that can be achieved by classifying the polarity of texts, in Brazilian Portuguese tweets. Although RWFSN reaches excellent performance in many PU learning problems, it has two major limitations when applied in our problem: it assumes that samples are long texts (many features) and that the class prior probabilities are known. We leverage the technique by augmenting the data representation in the feature space and by adding a validation set to better estimate the class priors. As a result, we identified unlabeled samples of the positive class with precision around at 70% in higher labeled ratio, but with high standard deviation, showing the impact of data variance in results. Moreover, given the properties of the RWFSN method, we provide interpretability of the results by pointing out the most relevant features of the task.
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13 October 2020
Inadvertently the authors of this chapter released it without correcting an error in the title. This has now been corrected and the corrected title reads: “Semi-Supervised Sentiment Analysis of Portuguese Tweets with Random Walk in Feature Sample Networks”.
Notes
- 1.
Available on https://github.com/pedrogengo/RWFSN.
References
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media, LSM 2011, pp. 30–38. Association for Computational Linguistics, Stroudsburg (2011). http://dl.acm.org/citation.cfm?id=2021109.2021114
Brum, H.B., das Graças Volpe Nunes, M.: Building a sentiment corpus of tweets in brazilian portuguese (2017). CoRR abs/1712.08917 http://arxiv.org/abs/1712.08917
Corrêa Jr, E.A., Marinho, V.Q., Santos, L.B.D., Bertaglia, T.F.C., Treviso, M.V., Brum, H.B.: Pelesent: Cross-domain polarity classification using distant supervision (2017)
Dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 69–78 (2014)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Proj. Rep. Stanford 1(12), 2009 (2009)
Kralj Novak, P., Smailović, J., Sluban, B., Mozetič, I.: Sentiment of emojis. PLoS ONE 10(12), e0144296 (2015). https://doi.org/10.1371/journal.pone.0144296
Liu, B.: Sentiment Analysis and Opinion Mining, pp. 1–135. Cambridge University Press, New York (2015)
Muniz, M.C.M.: A construção de recursos lingüístico-computacionais para o português do brasil: o projeto de unitex-pb. São Carlos (2004)
Mũnoz-Marí, J., Bovolo, F., Gómez-Chova, L., Bruzzone, L., Camp-Valls, G.: Semisupervised one-class support vector machines for classification of remote sensing data. IEEE Trans. Geosci. Remote Sens. 48(8), 3188–3197 (2010)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. LREC 10, 1320–1326 (2010)
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)
Verri, F.A.N., Zhao, L.: Random walk in feature - sample networks for semi-supervised classification. In: 5th Brazilian Conference on Intelligent Systems Random, pp. 235–240 (2016). https://doi.org/10.1109/BRACIS.2016.41
Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 3(1), 1–130 (2009). https://doi.org/10.2200/S00196ED1V01Y200906AIM006
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This work was supported by Itaú-Unibanco.
Any opinions, findings, and conclusions expressed in this manuscript are those of the authors and do not necessarily reflect the views, official policy or position of Itaú-Unibanco.
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Gengo, P., Verri, F.A.N. (2020). Semi-Supervised Sentiment Analysis of Portuguese Tweets with Random Walk in Feature Sample Networks. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham. https://doi.org/10.1007/978-3-030-61377-8_42
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