A Big Data Experiment to Assess the Effectiveness of Deep Learning Neural Networks in the Mining of Sustainable Aspects of the Hotels Clients Opinions

  • Thiago de Oliveira Lima
  • Methanias Colaço Júnior
  • Kleber H. de J. Prado
  • Adalberto dos S. Júnior
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 800)


Context: Opinions given by hotel clients in tourism social networks, the ones which can be a great source of knowledge extraction in the Big Data context, including the sustainable aspects of the hotels clients opinions. Objective: Evaluate performance and quality of deep learning neural networks, especially the Target-Connection LSTM (TC-LSTM) and Attention-based LSTM (AT-LSTM) algorithms, aiming to mine and classify the opinions posted on the TripAdvisor and Booking social networks, by considering sustainability aspects. Method: A controlled experiment to compare the efficiency and efficacy of the classifiers was carried out. Results: The AT-LSTM algorithm presented the best results, especially in terms of accuracy, precision, f-measure, average training time and average classification time. The first with 74,58%, the second with 95,54%, the third with 85,37%, then fourth with 7,3 s and the last one with 1,12 s. Conclusion: The AT-LSTM algorithm was expressly more effective than TC-LSTM, making it an option to be considered for mining opinions based on specific aspects of tourism and peculiar market niches.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Thiago de Oliveira Lima
    • 1
  • Methanias Colaço Júnior
    • 1
  • Kleber H. de J. Prado
    • 1
    • 2
  • Adalberto dos S. Júnior
    • 1
    • 2
  1. 1.Postgraduate Program in Computer Science – PROCCFederal University of Sergipe (UFS)AracajuBrazil
  2. 2.Universidade Federal de Pelotas – Rio Grande do SulPelotasBrazil

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