An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era Obiedat, Ruba Al-Qaisi, Laila Qaddoura, Raneem Harfoushi, Osama Al-Zoubi, Ala´ M. Word embedding Sentiments Medical products Metaheuristic Pre-trained models Due to the accelerated growth of symmetrical sentiment data across different platforms, experimenting with different sentiment analysis (SA) techniques allows for better decision-making and strategic planning for different sectors. Specifically, the emergence of COVID-19 has enriched the data of people’s opinions and feelings about medical products. In this paper, we analyze people’s sentiments about the products of a well-known e-commerce website named Alibaba.com. People’s sentiments are experimented with using a novel evolutionary approach by applying advanced pre-trained word embedding for word presentations and combining them with an evolutionary feature selection mechanism to classify these opinions into different levels of ratings. The proposed approach is based on harmony search algorithm and different classification techniques including random forest, k-nearest neighbor, AdaBoost, bagging, SVM, and REPtree to achieve competitive results with the least possible features. The experiments are conducted on five different datasets including medical gloves, hand sanitizer, medical oxygen, face masks, and a combination of all these datasets. The results show that the harmony search algorithm successfully reduced the number of features by 94.25%, 89.5%, 89.25%, 92.5%, and 84.25% for the medical glove, hand sanitizer, medical oxygen, face masks, and whole datasets, respectively, while keeping a competitive performance in terms of accuracy and root mean square error (RMSE) for the classification techniques and decreasing the computational time required for classification. 2021-12-09T10:53:27Z 2021-12-09T10:53:27Z 2021 journal article Obiedat, R.; Al-Qaisi, L.; Qaddoura, R.; Harfoushi, O.; Al-Zoubi, A.M. An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era. Symmetry 2021, 13, 2287. https://doi.org/Obiedat, R.; Al-Qaisi, L.; Qaddoura, R.; Harfoushi, O.; Al-Zoubi, A.M. An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era. Symmetry 2021, 13, 2287. https://doi.org/10.3390/ sym13122287 http://hdl.handle.net/10481/71937 10.3390/sym13122287 eng http://creativecommons.org/licenses/by/3.0/es/ open access Atribución 3.0 España MDPI