Analyzing Booking's Comments Using Stacking Ensemble Deep Learning Model and Neural Topic Model

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Analyzing Booking's Comments Using Stacking Ensemble Deep Learning Model and Neural Topic Model 18 Pages Posted: 4 Aug 2022 See all articles by Nassera HABBATNassera HABBATaffiliation not provided to SSRNHouda ANOUNaffiliation not provided to SSRNLarbi HASSOUNIaffiliation not provided to SSRNHicham NOURIaffiliation not provided to SSRN Abstract One of the most significant commercial uses of the internet today is for planning and booking. User-generated comments, reviews, and reports about their trips are becoming a more critical source of information with the emergence of Web 2.0. Such reviews are crucial when booking hotels because they are more accurate and comprehensive than those found in traditional printed recommendations for hotels ..., etc. They are unaffected by marketing factors like the home pages or catalogue descriptions of hotels, which represent genuine guest experiences. Therefore, hotel management can use these comments to maintain quality control. We present a system that collects comments from the Booking website concerning some tourist cities in Morocco and generates classified and structured overviews of such comments, as well as discovering topics in each class (positive and negative) to identify actionable insights from the reviews and aid the hotel in improving guest satisfaction. In the proposed technique, first, we used XLNet for French languages to get word embedding from the collected dataset; after that, we constructed four deep learning models; LSTM, CNN, GRU, and BiLSTM take as input the word embeddings. The outputs of these algorithms are then integrated using the stacking ensemble method, with a Multilayer perceptron serving as a meta-learner. After obtaining the two classes, we constructed a neural topic model to extract topics from each. The results of applying the suggested method to several datasets in Arabic and French reveal that our approach enhanced accuracy by 0.975 when compared to basic deep learning methods using various contextualized word embedding models, namely GPT and BERT. Keywords: BERT, Deep learning, GPT, Neural topic model, Sentiment analysis, Stacked deep learning model. XLNet Suggested Citation: Suggested Citation HABBAT, Nassera and ANOUN, Houda and HASSOUNI, Larbi and NOURI, Hicham, Analyzing Booking's Comments Using Stacking Ensemble Deep Learning Model and Neural Topic Model. Available at SSRN: https://ssrn.com/abstract=4181357 Nassera HABBAT (Contact Author) affiliation not provided to SSRN ( email ) No Address Available Houda ANOUN affiliation not provided to SSRN ( email ) No Address Available Larbi HASSOUNI affiliation not provided to SSRN ( email ) No Address Available Hicham NOURI affiliation not provided to SSRN ( email ) No Address Available Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 0 Abstract Views 1 PlumX Metrics Related eJournals Data Science & Analytics eJournal Follow Data Science & Analytics eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 93 PAPERS 1,624 Libraries & Information Technology eJournal Follow Libraries & Information Technology eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 44 PAPERS 3,202 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.316 seconds