27 Citations (Scopus)


In the industrial era 5.0, product reviews are necessary for the sustainability of a company. Product reviews are a User Generated Content (UGC) feature which describes customer satisfaction. The researcher used five hotel aspects including location, meal, service, comfort, and cleanliness to measure customer satisfaction. Each product review was preprocessed into a term list document. In this context, we proposed the Probabilistic Latent Semantic Analysis (PLSA) method to produce a hidden topic. Semantic Similarity was used to classify topics into five hotel aspects. The Term Frequency-Inverse Corpus Frequency (TF-ICF) method was used for weighting each term list, which had been expanded from each cluster in the document. The researcher used Word embedding to obtain vector values in the deep learning method from Long Short-Term Memory (LSTM) for sentiment classification. The result showed that the combination of the PLSA + TF ICF 100% + Semantic Similarity method was superior are 0.840 in the fifth categorization of the hotel aspects; the Word Embedding + LSTM method outperformed the sentiment classification at value 0.946; the service aspect received positive sentiment value higher are 45.545 than the other aspects; the comfort aspect received negative sentiment value higher are 12.871 than the other aspects. Other results also showed that sentiment was affected by the aspects.

Original languageEnglish
Pages (from-to)275-290
Number of pages16
JournalInternational Journal of Intelligent Engineering and Systems
Issue number4
Publication statusPublished - 2019


  • Aspect categorization
  • Customer satisfaction
  • Deep learning
  • Hidden topic
  • Hotel aspects
  • Product reviews
  • Semantic similarity
  • Sentiment classification
  • Term list document
  • Word embedding


Dive into the research topics of 'Sentiment analysis of hotel aspect using probabilistic latent semantic analysis, word embedding and LSTM'. Together they form a unique fingerprint.

Cite this