AspectSA: Unsupervised system for aspect based sentiment analysis in Spanish



Palabras clave:

Sentiment Analysis, Unsupervised, Aspect Based, Opinion Mining, NLP


This paper describes an unsupervised system for sentiment analysis is presented in Spanish. The system performs a complete fine grain analysis, where the most important characteristics or aspects of an opinion are identified in order to determine their sentiment associated. The unsupervised approach used allows to extract, identify and sentiment classify, from the analysis of opinions in Spanish in a specific domain, allowing to scale to another language and domain with great ease. For the validation of AspectSA, several experiments were carried out using corpus of opinions in the restaurant domain. The results obtained exceeded the majority of existing systems for the Spanish language.


C. Henríquez and J. Guzmán, “Extracción de información desde la web para identificar acciones de un modelo de dominio en planificación automática.,” Ingeniare, vol. 23, no. 3, pp. 439–448, 2015.

C. Henríquez and J. A. Guzmán, “Modelo de extracción de información desde recursos web para aplicaciones de la planificación automática,” Prospectiva, vol. 10, no. 2, pp. 74–80, 2012.

B. Liu, Sentiment analysis: Mining opinions, sentiments, and emotions. 2015.

C. Henríquez, J. Guzmán, and D. Salcedo, “Minería de Opiniones basado en la adaptación al español de ANEW sobre opiniones acerca de hoteles Opinion,” Proces. del Leng. Nat., vol. 41, pp. 25–32, 2016.

B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Found. Trends Inf. Retr., vol. 2, no. 12, pp. 1–135, 2008.

C. Henríquez, F. Plà, L. F. Hurtado, and J. A. G. Luna, “Análisis de sentimientos a nivel de aspecto usando ontologías y aprendizaje automático,” Proces. del Leng. Nat., vol. 59, pp. 49–56, 2017.

J. Steinberger, T. Brychcín, and M. Konkol, “Aspect-Level Sentiment Analysis in Czech,” in Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2014, pp. 24–30.

F. Ali, D. Kwak, P. Khan, S. M. R. Islam, K. H. Kim, and K. S. Kwak, “Fuzzy Ontology-based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling,” Transp. Res. Part C Emerg. Technol., vol. 77, pp. 33–48, 2017.

R. Hoogervorst et al., “Aspect-Based Sentiment Analysis on the Web using Rhetorical Structure Theory,” Int. Conf. Web Eng. Springer Int., 2016.

T. A. Rana and Y.-N. Cheah, “Aspect extraction in sentiment analysis: comparative analysis and survey,” Artif. Intell. Rev., vol. 46, no. 4, pp. 459–483, 2016.

C. Henríquez and J. Guzmán, “A Review of Sentiment Analysis in Spanish,” Tecciencia, vol. 12, no. 22, pp. 35–48, 2017.

L. Sun, S. Li, J. Li, and J. Lv, “A novel context-based implicit feature extracting method,” in Data Science and Advanced Analytics (DSAA), 2014 International Conference on, 2014, pp. 420–424.

W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., 2014.

K. Schouten and F. Frasincar, “Finding Implicit Features in Consumer Reviews for Sentiment Analysis,” in Web Engineering: 14th International Conference, ICWE 2014, Toulouse, France, July 1-4, 2014. Proceedings, S. Casteleyn, G. Rossi, and M. Winckler, Eds. Cham: Springer International Publishing, 2014, pp. 130–144.

I. Peñalver-Martinez et al., “Feature-based opinion mining through ontologies,” Expert Syst. with Appl. , vol. 41, no. 13, pp. 5995–6008, 2014.

H. Wang, Y. Lu, and C. Zhai, “Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach,” 2010.

W. Zhang, H. Xu, and W. Wan, “Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis,” Expert Syst. Appl., 2012.

G. Qiu, B. Liu, J. Bu, and C. Chen, “Opinion Word Expansion and Target Extraction through Double Propagation,” Comput. Linguist., vol. 37, pp. 9–27, 2011.

D. Marcheggiani, O. Täckström, A. Esuli, and F. Sebastiani, “Hierarchical multi-label conditional random fields for aspect-oriented opinion mining,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014.

F. Xianghua, L. Guo, G. Yanyan, and W. Zhiqiang, “Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon,” Knowledge-Based Systems. 2013.

E. Cambria, “An Introduction to Concept-Level Sentiment Analysis.,” in MICAI (2), 2013, pp. 478–483.

K. Schouten and F. Frasincar, “Survey on Aspect-Level Sentiment Analysis,” IEEE Trans. Knowl. Data Eng., vol. 28, no. 3, 2016.

L.-F. Hurtado and F. Pla, “ELiRF-UPV at TASS 2016: Sentiment Analysis in Twitter,” in TASS 2016: Workshop on Sentiment Analysis at SEPLN, 2016, pp. 47–51.

M. Pontiki et al., “SemEval-2016 Task 5: Aspect Based Sentiment Analysis,” in Semeval, 2016, pp. 19–30.

L. A. De Freitas and R. Vieira, “Ontology-based Feature Level Opinion Mining for Portuguese Reviews,” in Proceedings of the 22nd International Conference on World Wide Web. ACM, 2013, pp. 367–370.

A. S. Manek, P. D. Shenoy, and M. C. Mohan, “Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier,” World Wide Web, pp. 1–20, 2016.

S. and V. P. and K. G. and C. E. and Z. R. Hazarika Devamanyuand Poria, “Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) , 2018, pp. 266–270.

L. A. Jiménez-Zafra, S. M., Martín-Valdivia, M. T., Martínez-Cámara, E., & Ureña-López, “Combining resources to improve unsupervised sentiment analysis at aspect-level,” J. Inf. Sci., 2015.

C. Wu, F. Wu, S. Wu, Z. Yuan, and Y. Huang, “A hybrid unsupervised method for aspect term and opinion target extraction,” Knowledge-Based Syst., vol. 148, pp. 66–73, May 2018.

A. García-Pablos, M. Cuadros, and G. Rigau, “W2vlda: almost unsupervised system for aspect based sentiment analysis,” Expert Syst. Appl., vol. 91, pp. 127–137, 2018.

L. Padró and E. Stanilovsky, “FreeLing 3.0: Towards Wider Multilinguality,” in LREC2012, 2012.

A. Gonzalez-Agirre and G. Rigau, “Construcción de una base de conocimiento léxico multilíngüe de amplia cobertura: Multilingual Central Repository Building a wide coverage multilingual lexical knowledge base: Multilingual Central Repository,” Linguamatica, vol. 5, no. 1, pp. 13–28, 2013.

K. W. Church and P. Hanks, “Word association norms, mutual information, and lexicography,” Comput. Linguist., vol. 16, no. 1, pp. 22–29, 1990.

A. Kumar, S. Kohail, A. Kumar, A. Ekbal, and C. Biemann, “IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis,” in Proceedings of SemEval, 2016, pp. 1129–1135.