Publicação
Topic modeling: Summarize and organize data corpus using machine learning algorithms
| Resumo: | The research compares the results and performance of the Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) models of Machine Learning when applied Topic Modeling in documents of formal channels of scientific communication, consisting of 2006 scientific articles and expanded abstracts from the XIII to the XVII National Meeting of Research in Information Science (ENANCIB). The steps of empirical research are the collection of data for the constitution, cleaning, manipulation, combination, normalization, treatment and transformation of data from the corpus to connect to machine learning models. The models summarized and organized the data corpus into topics that are made up of terms and weights. The LSI model presented a greater variety between the terms and weights contained in each topic, different from the LDA model which presented a greater similarity in the results, thus making it easier for the domain specialist to create the assumption for the names of the topics. |
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| Autores principais: | Souza , Marcos de |
| Outros Autores: | Souza , Renato Rocha |
| Assunto: | Modelagem de tópicos Aprendizagem de máquina Alocação de Dirichlet Latente Indexação semântica latente Modeling topics Machine learning Latent Dirichlet allocation Latent semantic indexing |
| Ano: | 2020 |
| País: | Brasil |
| Tipo de documento: | artigo |
| Tipo de acesso: | unknown |
| Instituição associada: | IBICT |
| Idioma: | português |
| Origem: | Oasisbr |
| Resumo: | The research compares the results and performance of the Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) models of Machine Learning when applied Topic Modeling in documents of formal channels of scientific communication, consisting of 2006 scientific articles and expanded abstracts from the XIII to the XVII National Meeting of Research in Information Science (ENANCIB). The steps of empirical research are the collection of data for the constitution, cleaning, manipulation, combination, normalization, treatment and transformation of data from the corpus to connect to machine learning models. The models summarized and organized the data corpus into topics that are made up of terms and weights. The LSI model presented a greater variety between the terms and weights contained in each topic, different from the LDA model which presented a greater similarity in the results, thus making it easier for the domain specialist to create the assumption for the names of the topics. |
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