| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Kumar, Mukesh
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (11/11 displayed)
- 2024Unveiling the Impact of Processing Parameters on Microstructure and Mechanical Properties of Hybrid Composites Developed via Friction Stir Processingcitations
- 2024Impact response of filament-wound structure with polymeric liner: Experimental and numerical investigation (Part-A)citations
- 2024In situ ex-solution of CoFeRu solid solution nanoparticles from non-stoichiometric (La$_{0.8}$Sr$_{0.2}$)$_{0.9}$Co$_{0.1}$Fe$_{0.8}$Ru$_{0.1}$O$_{3−δ}$ perovskite for hydrogen gas sensor
- 2023Improving Biocompatibility for Next Generation of Metallic Implants.citations
- 2023Novel technique of vibration minimization during hard machiningcitations
- 2023Efficient Hotel Review Rating Prediction using Ensemble Learningcitations
- 2023Parametric optimization and ranking analysis of basalt fiber–marble dust particulates–polyamide 66 polymer composites under dry sliding wear investigationcitations
- 2022Microstructure, Mechanical, and Nanotribological Properties of Ni, Ni-TiN, and Ni90Cu10-TiN Films Processed by Reactive Magnetron Cosputteringcitations
- 2019Effect of Artificial Aging Temperature on Mechanical Properties of 6061 Aluminum Alloy
- 2016Cu–Zn disorder and band gap fluctuations in Cu2ZnSn(S,Se)4 : Theoretical and experimental investigationscitations
- 2012Band Gap and Conductivity Measurement of TiO<sub>2</sub> Thin Films Deposited by Sol-Gel Spin Coating Methodcitations
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document
Efficient Hotel Review Rating Prediction using Ensemble Learning
Abstract
<jats:title>Abstract</jats:title><jats:p>Machine Learning (ML) is a branch of Artificial Intelligence (AI) in which data-driven schemes learn patterns by being exposed to relevant data. Natural Language Processing (NLP) has been benefiting greatly from ML. In this article, ML is used to anticipate hotel review ratings. This paper proposes an Ensemble Learning-based rating prediction model for predicting ratings based on user reviews. The main objective of this research article is to accurately predict the best user experience rating. The majority voting technique of Ensemble learning has been used to predict the ratings. In this research article, first, the dataset is cleaned and then processed through a series of Natural Language Processing (NLP) preprocessing steps. This article includes a comparison of various classifiers with various embedders. Stochastic Gradient Descent (SGD) Classifier, Logistic Regression (LR), Logistic Regression Cross Validation (LRCV), Support Vector Classifier (SVC), Decision Tree Classifier (DTC), Random Forest Classifier (RFC), and K-Nearest Neighbour (KNN) are the 7 classifiers used, along with 3 embedding techniques: Bag of Words (BoW), Word2Vec, and Term Frequency-Inverse Document Frequency (TF-IDF). Our proposed solution is more accurate and works well. Accuracy and count are used as performance measures to compare and validate classifiers and embedders. According to the simulation report, TF-IDF utilizing LRCV has a 61% accuracy rate.</jats:p>