Arabic Language Sentiment Analysis on Health Services

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

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Abstract

The social media network phenomenon creates massive amounts of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited.

This paper introduces an Arabic language dataset, which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naïve Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.


Publisher Statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Original languageEnglish
Title of host publication2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR)
PublisherIEEE Computer Society
Pages114-118
Number of pages5
ISBN (Electronic)978-1-5090-6628-5
ISBN (Print)978-1-5090-6629-2
DOIs
Publication statusPublished - 16 Oct 2017
EventInternational Workshop on Arabic and derived Script Analysis and Recognition - University Lorraine, Nancy, France
Duration: 3 Apr 20175 Apr 2017
Conference number: 1
http://www.teklia.com/?p=220

Workshop

WorkshopInternational Workshop on Arabic and derived Script Analysis and Recognition
Abbreviated titleASAR
CountryFrance
CityNancy
Period3/04/175/04/17
Internet address

Fingerprint

Health
Learning algorithms
Support vector machines
Learning systems
Logistics
Marketing
Servers
Neural networks
Processing
Experiments

Keywords

  • Twitter
  • Sentiment analysis
  • Filtering
  • Feature extraction
  • Support vector machines
  • Semantics
  • Neural networks
  • Bayes methods
  • Big Data
  • data mining
  • learning (artificial intelligence)
  • neural nets
  • pattern classification
  • regression analysis
  • sentiment analysis
  • social networking (online)
  • support vector machines
  • Arabic language sentiment analysis
  • health services
  • social media network phenomenon
  • tweets
  • Arabic language dataset
  • big sentiment analysis dataset
  • Convolutional Neural Networks
  • health dataset
  • Arabic text filtering
  • Arabic text annotation
  • Arabic text preprocessing
  • machine learning algorithms
  • naïve Bayes algorithm
  • support vector machine algorithm
  • logistic regression algorithm
  • deep neural network
  • Sentiment Analysis
  • Machine Learning
  • Deep Neural NEtworks
  • Arabic Language

Cite this

Alayba, A., Palade, V., England, M., & Iqbal, R. (2017). Arabic Language Sentiment Analysis on Health Services. In 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) (pp. 114-118). IEEE Computer Society. https://doi.org/10.1109/ASAR.2017.8067771

Arabic Language Sentiment Analysis on Health Services. / Alayba, Abdulaziz; Palade, Vasile; England, Matthew; Iqbal, Rahat.

2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR). IEEE Computer Society, 2017. p. 114-118.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Alayba, A, Palade, V, England, M & Iqbal, R 2017, Arabic Language Sentiment Analysis on Health Services. in 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR). IEEE Computer Society, pp. 114-118, International Workshop on Arabic and derived Script Analysis and Recognition, Nancy, France, 3/04/17. https://doi.org/10.1109/ASAR.2017.8067771
Alayba A, Palade V, England M, Iqbal R. Arabic Language Sentiment Analysis on Health Services. In 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR). IEEE Computer Society. 2017. p. 114-118 https://doi.org/10.1109/ASAR.2017.8067771
Alayba, Abdulaziz ; Palade, Vasile ; England, Matthew ; Iqbal, Rahat. / Arabic Language Sentiment Analysis on Health Services. 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR). IEEE Computer Society, 2017. pp. 114-118
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abstract = "The social media network phenomenon creates massive amounts of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset, which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Na{\"i}ve Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset. Publisher Statement: {\circledC} 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
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N2 - The social media network phenomenon creates massive amounts of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset, which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naïve Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset. Publisher Statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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