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

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

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

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

Research output: Contribution to journal › Article › peer-review

36 Citations (SciVal)

Original language	English
Journal	Applied Sciences (Switzerland)
Volume	12
Issue number	11
Publication status	Published (VoR) - 30 May 2022

Keywords

sentiment analysis; machine learning; classification; ensemble learning; feature selection

Access to Document

https://www.mdpi.com/2076-3417/12/11/5547

Cite this

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

keywords = "sentiment analysis; machine learning; classification; ensemble learning; feature selection",

author = "Mohammed Hadwan and Mohammed Al-Sarem and Faisal Saeed and Mohammed Al-Hagery",

year = "2022",

month = may,

day = "30",

language = "English",

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journal = "Applied Sciences (Switzerland)",

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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 et al.
In: Applied Sciences (Switzerland), Vol. 12, No. 11, 30.05.2022.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services? Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

AU - Hadwan, Mohammed

AU - Al-Sarem, Mohammed

AU - Saeed, Faisal

AU - Al-Hagery, Mohammed

PY - 2022/5/30

Y1 - 2022/5/30

KW - sentiment analysis; machine learning; classification; ensemble learning; feature selection

M3 - Article

SN - 2076-3417

VL - 12

JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

IS - 11

ER -

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

Keywords

Access to Document

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Cite this