Abstract
In this paper, we describe our participation in the Rest-Mex 2022 forum for the Sentiment Analysis task. The objective of the task was to create a model capable of predicting the polarity of the sentiment expressed by a tourist’s opinion, as well as the type of attraction visited. For this task, we followed two different approaches: a lexicon-based approach and a Machine Learning approach. In the lexicon-based approach, we use a dictionary with words that have a numerical value that specifies the association with some emotions or attractions. We trained a logistic regression model for the Machine Learning approach to predict sentiment polarity and attractions. Our proposal obtained a combined score for both tasks of 0.85, which is only 0.03 away from the best reported result.
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Notes
- 1.
- 2.
Distributions of classes in the test set were not provided by the forum organizers.
- 3.
We thank Gustavo-Alain Peduzzi-Acevedo, Edgar-Josue Varillas-Figueroa, Juan-Daniel Del-Valle-Pérez and Francisco-Javier Aragón-González for their help in implementing this algorithm.
- 4.
Results were published in the official web page https://sites.google.com/cicese.edu.mx/rest-mex-2022/results?authuser=0.
- 5.
References
Cheung, C.M., Lee, M.K., Rabjohn, N.: The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities. Internet Res. (2008)
Taboada, M., Brooke, J., Tofiloski, M., Voll, K.D., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37, 267–307 (2011)
Mukhtar, N., Khan, M.A.: Effective lexicon-based approach for Urdu sentiment analysis. Artif. Intell. Rev. 53, 2521–2548 (2020)
Mowlaei, M.E., Abadeh, M.S., Keshavarz, H.: Aspect-based sentiment analysis using adaptive aspect-based Lexicons. Expert Syst. Appl. 148, 113234 (2020)
Hu, M., Liu, B.: Mining opinion features in customer reviews. In: AAAI 2004, pp. 755–760. AAAI Press (2004)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs Up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, Association for Computational Linguistics (2002)
Gambino, O.J., Calvo, H.: A comparison between two Spanish sentiment lexicons in the twitter sentiment analysis task. In: Montes-y-Gómez, M., Escalante, H.J., Segura, A., Murillo, J.D. (eds.) IBERAMIA 2016. LNCS (LNAI), vol. 10022, pp. 127–138. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47955-2_11
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, European Language Resources Association (2010)
Wilson, T., et al.: OpinionFinder: a system for subjectivity analysis. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-2005) Companion Volume (software demonstration) (2005)
Stone, P.J.: The General Inquirer: A Computer Approach to Content Analysis. The MIT Press, Cambridge (1966)
Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29, 24–54 (2010)
Rangel, I.D., Guerra, S.S., Sidorov, G.: Creación y evaluación de un diccionario marcado con emociones y ponderado para el español. Onomázein 29, 31–46 (2014)
Padró, L., Stanilovsky, E.: FreeLing 3.0: towards wider multilinguality. In: Proceedings of the Language Resources and Evaluation Conference, Istanbul, Turkey, ELRA (2012)
Bartz-Beielstein, T., Branke, J., Mehnen, J., Mersmann, O.: Evolutionary algorithms. WIREs Data Min. Knowl. Disc. 4, 178–195 (2014)
Keshavarz, H., Abadeh, M.S.: ALGA: adaptive lexicon learning using genetic algorithm for sentiment analysis of microblogs. Knowl. Based Syst. 122, 1–16 (2017)
Machová, K., Mikula, M., Gao, X., Mach, M.: Lexicon-based sentiment analysis using the particle swarm optimization. Electronics 9, 1317 (2020)
Sourabh, K., Singh, C.S., Vijay, K.: A review on genetic algorithm: past, present, and future. Multimed. Tools App. 80, 8091–8126 (2021)
Fernández, A., García, S., Galar, M., Prati, R.C., Krawczyk, B., Herrera, F.: Learning from Imbalanced Data Sets. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98074-4
Estabrooks, A., Jo, T., Japkowicz, N.: A multiple resampling method for learning from imbalanced data sets. Comput. Intell. 20, 18–36 (2004)
Lemaître, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18, 1–5 (2017)
Acknowledgments
We thank the support of Insituto Politécnico Nacional (IPN), ESCOM-IPN, SIP-IPN projects numbers: SIP-20220620, SIP-2083, SIP-20220925 COFAA-IPN, EDI-IPN and CONACyT-SNI.
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Castillo-Montoya, JA., Gómez-Pérez, JF., Rosales-Onofre, T., Torres-López, MA., Gambino, O.J. (2022). Sentiment Analysis in the Rest-Mex Challenge. In: Pichardo Lagunas, O., Martínez-Miranda, J., Martínez Seis, B. (eds) Advances in Computational Intelligence. MICAI 2022. Lecture Notes in Computer Science(), vol 13613. Springer, Cham. https://doi.org/10.1007/978-3-031-19496-2_11
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