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Extensive hotel reviews classification using long short term memory

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Abstract

Reviews of users on social networks have been gaining rapidly interest on the usage of sentiment analysis which serve as feedback to the government, public and private companies. Text Mining has a wide variety of applications such as sentiment analysis, spam detection, sarcasm detection, and news classification. Reviews classification using user sentiments is an important and collaborative task for many organizations. During recent years, text classification is mostly studied with machine learning models and hand–crafted features which are not able to give promising results on short text classification. In this research, a deep neural network–based model Long Short Term Memory (LSTM) with word embedding features is proposed. The proposed model has been evaluated on the large dataset of Hotel reviews based on accuracy, precision, recall, and F1-score. This research is a classification study on the hotel review sentiments given by guests of the hotel. The results reveal that the proposed model performs better as compared to the existing state-of-the-art models when combined word embedding with LSTM and shows an accuracy of 97%, precision 83%, recall 71%, and F1-score 76.53%. These promising results reveal the effectiveness of the proposed model on any type of review classification tasks.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1A2C1006159), MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2020-2016-0-00313) supervised by the IITP(Institute for Information & communications Technology Promotion), The Brain Korea 21 Plus Program(No. 22A20130012814) funded by the National Research Foundation of Korea (NRF), and in part by the Fareed Computing Research Center, Department of Computer Science under Khwaja Fareed University of Engineering and Information Technology(KFUEIT), Punjab, Rahim Yar Khan, Pakistan.

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Authors and Affiliations

  1. Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan

    Abid Ishaq, Muhammad Umer & Hafeez Ur Rehman Siddiqui

  2. Department of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan

    Muhammad Faheem Mushtaq

  3. Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan

    Arif Mehmood

  4. Research Department, Link Campus University of Rome, Via del Casale di San Pio V, 44, 00165, Rome, Italy

    Carlo Medaglia

  5. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea

    Gyu Sang Choi

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  1. Abid Ishaq
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Correspondence to Muhammad Umer, Muhammad Faheem Mushtaq, Arif Mehmood or Gyu Sang Choi.

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”The authors declare no conflict of interest. The funding agency had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results”.

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Ishaq, A., Umer, M., Mushtaq, M.F. et al. Extensive hotel reviews classification using long short term memory. J Ambient Intell Human Comput 12, 9375–9385 (2021). https://doi.org/10.1007/s12652-020-02654-z

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