Learning rules for automatic identification of implicit aspects in Portuguese
Resumo
Este trabalho de análise de sentimentos está focado na tarefa de identificação de aspectos, dando ênfase aos chamados aspectos implícitos, ou seja, aqueles que não são mencionados explicitamente nos textos. Para isso, analisamos métodos baseados em frequência, adaptamos regras da língua inglesa para o português e desenvolvemos um método que aprende novas regras por meio de análise de corpus.Referências
Balage Filho, P. P. (2017). Aspect extraction in sentiment analysis for portuguese language. PhD thesis, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, Brazil.
Cai, H., Tu, Y., Zhou, X., Yu, J., and Xia, R. (2020). Aspect-category based sentiment In Proceedings of the 28th analysis with hierarchical graph convolutional network. International Conference on Computational Linguistics, pages 833–843, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cambria, E., Olsher, D., and Rajagopal, D. (2014). Senticnet 3: a common and commonsense knowledge base for cognition-driven sentiment analysis. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 28.
Costa, R. W. M. and Pardo, T. A. S. (2020). Métodos baseados em léxico para extração de aspectos de opiniões em português. In Anais do IX Brazilian Workshop on Social Network Analysis and Mining, pages 61–72. SBC.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training arXiv preprint transformers for language understanding. of deep bidirectional arXiv:1810.04805.
Honnibal, M., Montani, I., Van Landeghem, S., and Boyd, A. (2020). spaCy: Industrialstrength Natural Language Processing in Python.
Hu, M. and Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168–177. ACM.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1):1–167.
López Condori, R. E. and Pardo, T. A. S. (2017). Opinion summarization methods: Comparing and extending extractive and abstractive approaches. Expert Systems with Applications, 78:124–134.
Machado, M. T., Pardo, T. A. S., and Ruiz, E. E. S. (2017). Analysis of unsupervised aspect term identification methods for portuguese reviews. Anais do XIV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), SBC, pages 239–249.
Marcacini, R. M., Rossi, R. G., Matsuno, I. P., and Rezende, S. O. (2018). Cross-domain aspect extraction for sentiment analysis: A transductive learning approach. Decision Support Systems, 114:70–80.
Medhat, W., Hassan, A., and Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4):1093–1113.
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J. (2013b). Distributed arXiv preprint representations of words and phrases and their compositionality. arXiv:1310.4546.
Panchendrarajan, R., Ahamed, N., Murugaiah, B., Sivakumar, P., Ranathunga, S., and Pemasiri, A. (2016). Implicit aspect detection in restaurant reviews using cooccurence of words. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 128–136.
Pavlopoulos, J. and Androutsopoulos, I. (2014). Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method. In Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)@ EACL, pages 44–52.
Pereira, D. A. (2020). A survey of sentiment analysis in the portuguese language. Artificial Intelligence Review, pages 1–29.
Poria, S., Cambria, E., Ku, L.-W., Senticnet, C. G., and Gelbukh, A. (2014). A RuleBased Approach to Aspect Extraction from Product Reviews. In Proceedings of the second workshop on natural language processing for social media (SocialNLP), pages 28–37.
Rana, T. A. and Cheah, Y.-N. (2016). Aspect extraction in sentiment analysis: comparative analysis and survey. Artificial Intelligence Review, 46(4):459–483.
Ravi, K. and Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89:14 – 46.
Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2:325–347.
Vargas, F. A. and Pardo, T. A. S. (2018). Aspect Clustering Methods for Sentiment Analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11122 LNAI:365–374.
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):1– 33.
Zhang, Y. and Zhu, W. (2013). Extracting implicit features in online customer reviews for opinion mining. In Proceedings of the 22nd International Conference on World Wide Web, pages 103–104.
Cai, H., Tu, Y., Zhou, X., Yu, J., and Xia, R. (2020). Aspect-category based sentiment In Proceedings of the 28th analysis with hierarchical graph convolutional network. International Conference on Computational Linguistics, pages 833–843, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cambria, E., Olsher, D., and Rajagopal, D. (2014). Senticnet 3: a common and commonsense knowledge base for cognition-driven sentiment analysis. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 28.
Costa, R. W. M. and Pardo, T. A. S. (2020). Métodos baseados em léxico para extração de aspectos de opiniões em português. In Anais do IX Brazilian Workshop on Social Network Analysis and Mining, pages 61–72. SBC.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training arXiv preprint transformers for language understanding. of deep bidirectional arXiv:1810.04805.
Honnibal, M., Montani, I., Van Landeghem, S., and Boyd, A. (2020). spaCy: Industrialstrength Natural Language Processing in Python.
Hu, M. and Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168–177. ACM.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1):1–167.
López Condori, R. E. and Pardo, T. A. S. (2017). Opinion summarization methods: Comparing and extending extractive and abstractive approaches. Expert Systems with Applications, 78:124–134.
Machado, M. T., Pardo, T. A. S., and Ruiz, E. E. S. (2017). Analysis of unsupervised aspect term identification methods for portuguese reviews. Anais do XIV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), SBC, pages 239–249.
Marcacini, R. M., Rossi, R. G., Matsuno, I. P., and Rezende, S. O. (2018). Cross-domain aspect extraction for sentiment analysis: A transductive learning approach. Decision Support Systems, 114:70–80.
Medhat, W., Hassan, A., and Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4):1093–1113.
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J. (2013b). Distributed arXiv preprint representations of words and phrases and their compositionality. arXiv:1310.4546.
Panchendrarajan, R., Ahamed, N., Murugaiah, B., Sivakumar, P., Ranathunga, S., and Pemasiri, A. (2016). Implicit aspect detection in restaurant reviews using cooccurence of words. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 128–136.
Pavlopoulos, J. and Androutsopoulos, I. (2014). Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method. In Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)@ EACL, pages 44–52.
Pereira, D. A. (2020). A survey of sentiment analysis in the portuguese language. Artificial Intelligence Review, pages 1–29.
Poria, S., Cambria, E., Ku, L.-W., Senticnet, C. G., and Gelbukh, A. (2014). A RuleBased Approach to Aspect Extraction from Product Reviews. In Proceedings of the second workshop on natural language processing for social media (SocialNLP), pages 28–37.
Rana, T. A. and Cheah, Y.-N. (2016). Aspect extraction in sentiment analysis: comparative analysis and survey. Artificial Intelligence Review, 46(4):459–483.
Ravi, K. and Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89:14 – 46.
Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2:325–347.
Vargas, F. A. and Pardo, T. A. S. (2018). Aspect Clustering Methods for Sentiment Analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11122 LNAI:365–374.
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):1– 33.
Zhang, Y. and Zhu, W. (2013). Extracting implicit features in online customer reviews for opinion mining. In Proceedings of the 22nd International Conference on World Wide Web, pages 103–104.
Publicado
29/11/2021
Como Citar
MACHADO, Mateus Tarcinalli; PARDO, Thiago Alexandre Salgueiro; RUIZ, Evandro Eduardo Seron; FELIPPO, Ariani Di.
Learning rules for automatic identification of implicit aspects in Portuguese. In: SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E DA LINGUAGEM HUMANA (STIL), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 82-91.
DOI: https://doi.org/10.5753/stil.2021.17787.
