Sentiment Analysis for Assessment of Hotel Services Review using Feature Selection Approach based-on Decision Tree

To get the best hotel accommodation equipped with great services is all what a tourist want. Hotel reviews found in social media sometimes become a reference to book a hotel room. The problem is there is sometimes inaccuracy in understanding the reviewer’s sentiment; therefore sentiment analysis approach is used in this study. The sentiment analysis approach use three algorithms within this article; Naïve Bayes, Support vector machines, and decision tree. The result of the experiment is that decision tree is the best algorithm, however the accuracy level still become a focus since it is not optimal. The purpose of this study is to find a hybrid sentiment analysis model of an intelligent application that can be used as a decision support for hotel service assessment recommendations problem. In this paper, we proposed a model which was developed using the feature selection (FS) approach, whereas the improvement of model accuracy was done using information gain (IG). In this study, the experiment was carried out through five stages, namely taking the research dataset in the form of hotel service assessment texts, data pre-processing, weighting, experimental models, and evaluation. Experiments were conducted to get the best accuracy on the proposed model, while the evaluations were carried out to determine the accuracy of the model. Based on the experimental results, the best accuracy level in the model is 88.54%.

@article{Apriliani2020,
  title     = {Sentiment Analysis for Assessment of Hotel Services Review using Feature Selection Approach based-on Decision Tree},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {4},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Dyah Apriliani and Taufiq Abidin and Edhy Sutanta and Amir Hamzah and Oman Somantri},
  doi       = {10.14569/IJACSA.2020.0110432},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110432}
}