Opinion Mining and Active Learning: a Comparison of Sampling Strategies

  • Douglas Vitório CIN-UFPE
  • Ellen Souza UFRPE
  • Adriano L. I. Oliveira CIN-UFPE

Resumo


Existem dois problemas principais ao executar a Mineração de Opinião (OM) com fluxos de dados: a falta de dados rotulados e a necessidade de atualizar o modelo de aprendizagem. As técnicas de OM mais usadas não podem lidar bem com esses desafios, portanto, uma alternativa é usar métodos semissupervisionados, como o Active Learning, que é um método para rotular apenas dados selecionados em vez de todo o conjunto de dados; no entanto, requer a escolha de uma estratégia de amostragem para selecionar os dados a serem rotulados. Neste artigo, avaliamos oito estratégias em dez conjuntos de dados, a fim de identificar os melhores para OM com fluxos do Twitter. De acordo com nossos experimentos, a estratégia Entropy mostrou os melhores resultados, mas seleciona um grande número de instâncias a serem rotuladas, exigindo uma investigação mais aprofundada.

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Publicado
09/07/2019
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VITÓRIO, Douglas; SOUZA, Ellen ; OLIVEIRA, Adriano L. I. . Opinion Mining and Active Learning: a Comparison of Sampling Strategies. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 8. , 2019, Belém. Anais do VIII Brazilian Workshop on Social Network Analysis and Mining. Porto Alegre: Sociedade Brasileira de Computação, july 2019 . p. 61-70. ISSN 2595-6094. DOI: https://doi.org/10.5753/brasnam.2019.6549.