An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

Hadwan, Mohammed; Al-Sarem, Mohammed; Saeed, Faisal; Al-Hagery, Mohammed Abdullah

doi:10.3390/app12115547

Cited by 16 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They reported that the K-Nearest Neighbor and Decision Tree classifiers outperformed in terms of accuracy by 78% and 60%. Support Vector Machine and Naïve Bayes classifiers accomplished 55% and 51% accuracy scores ( 72 , 73 ).…”

Section: Resultsmentioning

confidence: 99%

Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models

Stefanis,

Giorgi,

Kalentzis

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

The present research deals with sentiment analysis performed with Microsoft Azure Machine Learning Studio to classify Facebook posts on the Greek National Public Health Organization (EODY) from November 2021 to January 2022 during the pandemic. Positive, negative and neutral sentiments were included after processing 300 reviews. This approach involved analyzing the words appearing in the comments and exploring the sentiments related to daily surveillance reports of COVID-19 published on the EODY Facebook page. Moreover, machine learning algorithms were implemented to predict the classification of sentiments. This research assesses the efficiency of a few popular machine learning models, which is one of the initial efforts in Greece in this domain. People have negative sentiments toward COVID surveillance reports. Words with the highest frequency of occurrence include government, vaccinated people, unvaccinated, telephone communication, health measures, virus, COVID-19 rapid/molecular tests, and of course, COVID-19. The experimental results disclose additionally that two classifiers, namely two class Neural Network and two class Bayes Point Machine, achieved high sentiment analysis accuracy and F1 score, particularly 87% and over 35%. A significant limitation of this study may be the need for more comparison with other research attempts that identified the sentiments of the EODY surveillance reports of COVID in Greece. Machine learning models can provide critical information combating public health hazards and enrich communication strategies and proactive actions in public health issues and opinion management during the COVID-19 pandemic.

show abstract

Section: Resultsmentioning

confidence: 99%

Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models

Stefanis,

Giorgi,

Kalentzis

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Based on related works and practical considerations, the preprocessing phase of the texts written in the Arabic language should include several essential processing steps: data cleaning, normalization, and stemming [12,41]. They are effective steps that have a positive effect on the overall model's performance.…”

Section: Data Preprocessingmentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback on E-Learning Using a Hybrid Fuzzy Model

Alzaid,

Fkih

2023

Applied Sciences

View full text Add to dashboard Cite

It is crucial to analyze opinions about the significant shift in education systems around the world, because of the widespread use of e-learning, to gain insight into the state of education today. A particular focus should be placed on the feedback from students regarding the profound changes they experience when using e-learning. In this paper, we propose a model that combines fuzzy logic with bidirectional long short-term memory (BiLSTM) for the sentiment analysis of students’ textual feedback on e-learning. We obtained this feedback from students’ tweets expressing their opinions about e-learning. There were some ambiguous characteristics in terms of the writing style and language used in the collected feedback. It was written informally and not in adherence to standardized Arabic language writing rules by using the Saudi dialects. The proposed model benefits from the capabilities of the deep neural network BiLSTM to learn and also from the ability of fuzzy logic to handle uncertainties. The proposed models were evaluated using the appropriate evaluation metrics: accuracy, F1-score, precision, and recall. The results showed the effectiveness of our proposed model and that it worked well for analyzing opinions obtained from Arabic texts written in Saudi dialects. The proposed model outperformed the compared models by obtaining an accuracy of 86% and an F1-score of 85%.

show abstract

“…A comprehensive survey is presented by Alan et al (2019) [44] in which the diversity of hepatitis B virus in the population of Asia and Europe is explained in the most effective manner. Also, the development of a model is being done by using the machine learning approach.…”

Section: Ye Et Al (2003)mentioning

confidence: 99%

Intelligent Medical Diagnostic System for Hepatitis B

Singh¹,

Prashar²,

Singla³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

The hepatitis B virus is the most deadly virus, which significantly affects the human liver. The termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage; otherwise, it will become a severe problem and make a human liver suffer from the most dangerous diseases, such as liver cancer. In this paper, two medical diagnostic systems are developed for the diagnosis of this life-threatening virus. The methodologies used to develop these models are fuzzy logic and the neuro-fuzzy technique. The diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both developed models. The classification accuracy of a multilayered fuzzy inference system is 94%. The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference System (ANFIS) classifies the result corresponding to the given input is 95.55%. The comparison of both developed models on the basis of their performance parameters has been made. It is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic system. Furthermore, the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B disease. In other words, the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.

show abstract

An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

Cited by 16 publications

References 43 publications

Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models

Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models

Sentiment Analysis of Students’ Feedback on E-Learning Using a Hybrid Fuzzy Model

Intelligent Medical Diagnostic System for Hepatitis B

Contact Info

Product

Resources

About