@misc{10481/71937,
year = {2021},
url = {http://hdl.handle.net/10481/71937},
abstract = {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.},
publisher = {MDPI},
keywords = {Word embedding},
keywords = {Sentiments},
keywords = {Medical products},
keywords = {Metaheuristic},
keywords = {Pre-trained models},
title = {An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era},
doi = {10.3390/sym13122287},
author = {Obiedat, Ruba and Al-Qaisi, Laila and Qaddoura, Raneem and Harfoushi, Osama and Al-Zoubi, Ala´ M.},
}