Extensive hotel reviews classification using long short term memory
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ORIGINAL RESEARCH
Extensive hotel reviews classification using long short term memory Abid Ishaq1 · Muhammad Umer1 · Muhammad Faheem Mushtaq2 · Carlo Medaglia4 · Hafeez Ur Rehman Siddiqui1 · Arif Mehmood3 · Gyu Sang Choi5 Received: 7 July 2020 / Accepted: 27 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Hotel reviews analysis · Long short–term memory · Text mining · Deep learning · Machine learning
1 Introduction * Muhammad Umer [email protected] * Muhammad Faheem Mushtaq [email protected] * Arif Mehmood [email protected] * Gyu Sang Choi [email protected] Carlo Medaglia [email protected] 1
Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
2
Department of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
3
Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
4
Research Department, Link Campus University of Rome, Via del Casale di San Pio V, 44, 00165 Rome, Italy
5
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
With the growth of smartphones, online platforms(Google, Facebook, and Twitter, etc.) have become one of the most used mechanisms for social interaction that promotes users to share information and create opinions and ideas in the shape of reviews or expressions. These expressions serve as a user’s feedback that can be used to improve the quality of products and services to revise and devise the policies. The planning and booking for travel on the web have gained much
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