1. Proceedings
  2. I3M
  3. 2018
  4. EMSS
  5. 10.46354/i3m.2018.emss.050

Automatic detection of sentiments in tourist reviews by using Long Short-Term Memory recurrent neural networks

  • a Carlos A. Martín  ,
  • b Rosa María Aguilar  ,
  • c Jesús M. Torres  , 
  • d S. Díaz  
  • a b, c, d Department of Computer and Systems Engineering, University of La Laguna, 38200 La Laguna (Tenerife), Spain.
Cite as
Martin C.A., Aguilar R.M., Torres J.M., Díaz S. (2018). Automatic detection of sentiments in tourist reviews by using Long Short-Term Memory recurrent neural networks. Proceedings of the 30th European Modeling & Simulation Symposium (EMSS 2018), pp. 356-360. DOI: https://doi.org/10.46354/i3m.2018.emss.050

Abstract

This paper describes an algorithm for automatically identifying the sentiments expressed by tourists on electronic Word of Mouth platforms. Based on reviews published by tourists after staying in hotels, a classifier is trained using a Long Short-Term Memory neural network supervised learning algorithm. We present a use case for this method involving a group of hotels located on the island of Tenerife (Canary Islands).

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