Deep Learning Based Techniques for Sentiment Analysis: A Survey

Authors

  • Wael Etaiwi Princess Sumaya University for Technology
  • Dima Suleiman Information Technology Department, King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan
  • Arafat Awajan Computer Science Department, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan

DOI:

https://doi.org/10.31449/inf.v45i7.3674

Abstract

The automated representation of human language using a number of techniques is called Natural Language Processing (NLP). Improvements to NLP applications are important and can be done using a variety of methods, including graphs, deep neural networks, and word embedding. Sentiment classification, which attempts to automatically classify opinionated text as positive, negative, or neutral, is a fundamental activity of sentiment analysis. Sentiment analysis methods focused on deep learning over the past five years are analyzed in this review

Author Biographies

Wael Etaiwi, Princess Sumaya University for Technology

Dr. Wael Etaiwi is an assistant professor in the Department of Business Information Technology at Princess Sumaya University for Technology, Jordan. He received his BSc degree in Computer Information Systems from the Hashemite University in 2007, his MSc Degree in Computer Science in 2011 from Al Balqaa Applied University, and his Ph.D. in Computer Science from Princess Sumaya University for Technology in 2020. Dr. Al Etaiwi has 13 years of experience in software development and system analyst. His research interests include, but are not limited to, Artificial intelligence, Data mining, and Natural Language Processing.

Dima Suleiman, Information Technology Department, King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan

Dima Suleiman received her Bachelor and master degrees in Computer Science from University of Jordan, and her Ph.D. in Computer Science from Princess Sumaya University for Technology in 2020. She has 15 years of experience in teaching undergraduate and graduate students at the University of Jordan in Business Information Technology department. Her research interests are in the areas of algorithms, Natural Language Processing, Data science and data mining. 

Arafat Awajan, Computer Science Department, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan

Prof. Arafat Awajan is a Full Professor at Princess Sumaya University for Technology (PSUT). He received his PhD degree in Computer Science from the University of Franche-Comte, France in 1987. He has held various administrative and academic positions at the Royal Scientific Society and Princess Sumaya University for Technology. Head of the Department of Computer Science (2000 - 2003) Head of the Department of Computer Graphics and Animation (2005 - 2006) Dean of the King Hussein School for Information Technology (2004 - 2007) Director of the Information Technology Center, RSS (2008 - 2010) Dean of Student Affairs (2011 - 2014) Dean of the King Hussein School for Computing Sciences (2014-2017) He is currently the vice president of the university (PSUT) . His research interests include • Natural Language Processing • Arabic Text Mining • Digital Image Processing.  

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Published

2021-12-23

How to Cite

Etaiwi, W., Suleiman, D., & Awajan, A. (2021). Deep Learning Based Techniques for Sentiment Analysis: A Survey. Informatica, 45(7). https://doi.org/10.31449/inf.v45i7.3674

Issue

Vol. 45 No. 7 (2021): Online-only issue

Section

Online-only

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