Trie of Rules para Visualização de Textos de Redes Sociais
Resumo
A visualização de dados é essencial para interpretar grandes volumes de informação, especialmente textos de redes sociais. Este trabalho propõe o uso do método Trie of Rules como uma solução eficiente e interpretável para análise e visualização de textos curtos. A metodologia inclui pré-processamento textual e organização hierárquica dos termos relevantes, permitindo identificar padrões e relações de forma clara. Também foram propostas funcionalidades para melhorar a qualidade da análise dos dados, como o uso de um Large Language Model (LLM) para resumir o conteúdo de postagens selecionadas de redes sociais. Os resultados esperados indicam uma visualização limpa, flexível e eficaz, que busca superar as limitações das abordagens comuns.
Referências
Agrawal, R., Imieliński, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. SIGMOD Rec., 22(2):207–216.
ARM-interpretation (2021). Trie-of-rules: Visualization method for association rule mining (arm). Repositório no GitHub. Acessado em: 16 mar. 2026.
Bastian, M., Heymann, S., and Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. [link]. Acessado em: 16 mar. 2026.
Bird, S., Klein, E., and Loper, E. (2009). Natural Language Processing with Python: analyzing text with the natural language toolkit. O’Reilly Media, Inc. Acessado em: 16 mar. 2026.
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Chen, X., Chen, J., Wu, D., Xie, Y., and Li, J. (2016). Mapping the research trends by co-word analysis based on keywords from funded project. Procedia computer science, 91:547–555.
da Silva, F. C. C. (2019). Visualização de dados: passado, presente e futuro. Liinc em Revista, 15(2):205–223.
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Goldschmidt, R. and Passos, E. (2005). Data mining: um guia prático. Gulf Professional Publishing.
Grahne, G. and Zhu, J. (2003). High performance mining of maximal frequent itemsets. 6th International workshop on high performance data mining, 16:34.
Han, J., Pei, J., and Yin, Y. (2000). Mining frequent patterns without candidate generation. SIGMOD Rec., 29(2):1–12.
Heimerl, F., Lohmann, S., Lange, S., and Ertl, T. (2014). Word cloud explorer: Text analytics based on word clouds. 47th Hawaii International Conference on System Sciences, pages 1833–1842.
Honnibal, M. and Montani, I. (2026). spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing. Acessado em: 16 mar. 2026.
Insights, S. (2025). New global social media research summary 2025. [link]. Acessado em: 16 mar. 2026.
Jurafsky, D. and Martin, J. H. (2008). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall, Upper Saddle River, NJ, 2 edition.
Karthikeyan, T. and Ravikumar, N. (2014). A survey on association rule mining. International Journal of Advanced Research in Computer and Communication Engineering, 3(1):2278–1021.
Keim, D. A., Mansmann, F., Schneidewind, J., Thomas, J., and Ziegler, H. (2008). Visual Analytics: Scope and Challenges, pages 76–90. Springer Berlin Heidelberg, Berlin, Heidelberg.
Kudriavtsev, M., McCarren, A., Lee, H., and Bezbradica, M. (2024). Efficient visualization of association rule mining using the trie of rules.
Kumbhare, T. A. and Chobe, S. V. (2014). An overview of association rule mining algorithms. International Journal of Computer Science and Information Technologies, 5(1):927–930.
Mining, W. I. D. (2006). Data mining: Concepts and techniques. Morgan Kaufinann, 10(559-569):4.
Nadeau, D. and Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1):3–26.
Schütze, H., Manning, C. D., and Raghavan, P. (2008). Introduction to information retrieval, volume 39. Cambridge University Press Cambridge.
Shneiderman, B., Plaisant, C., Hesse, B., Heine, L., Rose, A., and Harkey, D. (1996). The eyes have it: A task by data type taxonomy for information visualizations. Proceedings of the IEEE Symposium on Visual Languages, pages 336–343.
Statista (2025). Social networks - statistics & facts. [link]. Acessado em: 16 mar. 2026.
Wattenberg, M. and Viégas, F. B. (2008). The word tree, an interactive visual concordance. IEEE transactions on visualization and computer graphics, 14(6):1221–1228.
ARM-interpretation (2021). Trie-of-rules: Visualization method for association rule mining (arm). Repositório no GitHub. Acessado em: 16 mar. 2026.
Bastian, M., Heymann, S., and Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. [link]. Acessado em: 16 mar. 2026.
Bird, S., Klein, E., and Loper, E. (2009). Natural Language Processing with Python: analyzing text with the natural language toolkit. O’Reilly Media, Inc. Acessado em: 16 mar. 2026.
Brusso, M. J. (2000). Access miner: uma proposta para a extração de regras de associação aplicada à mineração do uso da web. Master’s thesis, Universidade Federal do Rio Grande do Sul.
Chen, D. (2015). Online retail. DOI: 10.24432/C5BW33. Acessado em: 16 mar. 2026.
Chen, X., Chen, J., Wu, D., Xie, Y., and Li, J. (2016). Mapping the research trends by co-word analysis based on keywords from funded project. Procedia computer science, 91:547–555.
da Silva, F. C. C. (2019). Visualização de dados: passado, presente e futuro. Liinc em Revista, 15(2):205–223.
Domo (2023). Data never sleeps 11.0. [link]. Acessado em: 16 de mar. 2026.
Export Comments (2026). Export facebook, instagram, twitter, youtube, tiktok, vimeo comments to csv / excel. Acessado em: 16 mar. 2026.
Gan, Q., Zhu, M., Li, M., Liang, T., Cao, Y., and Zhou, B. (2014). Document visualization: an overview of current research. Wiley Interdisciplinary Reviews: Computational Statistics, 6(1):19–36.
Goldschmidt, R. and Passos, E. (2005). Data mining: um guia prático. Gulf Professional Publishing.
Grahne, G. and Zhu, J. (2003). High performance mining of maximal frequent itemsets. 6th International workshop on high performance data mining, 16:34.
Han, J., Pei, J., and Yin, Y. (2000). Mining frequent patterns without candidate generation. SIGMOD Rec., 29(2):1–12.
Heimerl, F., Lohmann, S., Lange, S., and Ertl, T. (2014). Word cloud explorer: Text analytics based on word clouds. 47th Hawaii International Conference on System Sciences, pages 1833–1842.
Honnibal, M. and Montani, I. (2026). spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing. Acessado em: 16 mar. 2026.
Insights, S. (2025). New global social media research summary 2025. [link]. Acessado em: 16 mar. 2026.
Jurafsky, D. and Martin, J. H. (2008). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall, Upper Saddle River, NJ, 2 edition.
Karthikeyan, T. and Ravikumar, N. (2014). A survey on association rule mining. International Journal of Advanced Research in Computer and Communication Engineering, 3(1):2278–1021.
Keim, D. A., Mansmann, F., Schneidewind, J., Thomas, J., and Ziegler, H. (2008). Visual Analytics: Scope and Challenges, pages 76–90. Springer Berlin Heidelberg, Berlin, Heidelberg.
Kudriavtsev, M., McCarren, A., Lee, H., and Bezbradica, M. (2024). Efficient visualization of association rule mining using the trie of rules.
Kumbhare, T. A. and Chobe, S. V. (2014). An overview of association rule mining algorithms. International Journal of Computer Science and Information Technologies, 5(1):927–930.
Mining, W. I. D. (2006). Data mining: Concepts and techniques. Morgan Kaufinann, 10(559-569):4.
Nadeau, D. and Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1):3–26.
Schütze, H., Manning, C. D., and Raghavan, P. (2008). Introduction to information retrieval, volume 39. Cambridge University Press Cambridge.
Shneiderman, B., Plaisant, C., Hesse, B., Heine, L., Rose, A., and Harkey, D. (1996). The eyes have it: A task by data type taxonomy for information visualizations. Proceedings of the IEEE Symposium on Visual Languages, pages 336–343.
Statista (2025). Social networks - statistics & facts. [link]. Acessado em: 16 mar. 2026.
Wattenberg, M. and Viégas, F. B. (2008). The word tree, an interactive visual concordance. IEEE transactions on visualization and computer graphics, 14(6):1221–1228.
Publicado
19/07/2026
Como Citar
ONOFRE, Luiza Bolonha Vieira; CIARELLI, Patrick Marques.
Trie of Rules para Visualização de Textos de Redes Sociais. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 15. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 227-240.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2026.23700.
