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A review on recent advances in deep learning for sentiment analysis: Performances, challenges and limitations

Now days the horizons of social online media keep expanding, the impacts they have on people are huge. For example, many businesses are taking advantage of the input from social media to advertise to specific target market. This is done by detecting and analyzing the sentiment (emotions, feelings, o...

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Main Authors: Islam, Md Shofiqul, Ngahzaifa, Ab. Ghani, Ahmed, Md Manjur
Format: Article
Language:English
Published: COMPUSOFT 2020
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/28974/1/A%20review%20on%20recent%20advances%20in%20deep%20learning.pdf
http://umpir.ump.edu.my/id/eprint/28974/
https://ijact.in/index.php/ijact/article/view/1175
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spelling my.ump.umpir.289742020-08-07T03:32:42Z http://umpir.ump.edu.my/id/eprint/28974/ A review on recent advances in deep learning for sentiment analysis: Performances, challenges and limitations Islam, Md Shofiqul Ngahzaifa, Ab. Ghani Ahmed, Md Manjur QA75 Electronic computers. Computer science Now days the horizons of social online media keep expanding, the impacts they have on people are huge. For example, many businesses are taking advantage of the input from social media to advertise to specific target market. This is done by detecting and analyzing the sentiment (emotions, feelings, opinions) in social media about any topic or product from the texts. There are numerous machine learning as well as natural language processing methods used to examine public opinions with low time complexity. Deep learning techniques, however, have become widely popular in recent times because of their high efficiency and accuracy. This paper provides a complete overview of the common deep learning frameworks used in sentiment analysis in recent time. We offer a taxonomical study of text representations, learning model, evaluation, metrics and implications of recent advances in deep learning architectures. We also added a special emphasis on deep learning methods; the key findings and limitations of different authors are discussed. This will hopefully help other researchers to do further development of deep learning methods in text processing especially for sentiment analysis. The research also presents the quick summaries of the most popular datasets, lexicons with their related research, performance and main features of the datasets. The aim of this survey is to emphasize the ability to solve text-based sentiment analysis challenges in deep learning architectures with successful achievement for accuracy, speed with context, syntactic and semantic meaning. This review paper analyzes uniquely with the progress and recent advances in sentiment analysis based on existing advanced methods and approach based on deep learning with their findings, performance comparisons and the limitations. COMPUSOFT 2020 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/28974/1/A%20review%20on%20recent%20advances%20in%20deep%20learning.pdf Islam, Md Shofiqul and Ngahzaifa, Ab. Ghani and Ahmed, Md Manjur (2020) A review on recent advances in deep learning for sentiment analysis: Performances, challenges and limitations. COMPUSOFT: An International Journal of Advanced Computer Technology, 9 (7). pp. 3775-3783. ISSN 2320-0790 https://ijact.in/index.php/ijact/article/view/1175
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Islam, Md Shofiqul
Ngahzaifa, Ab. Ghani
Ahmed, Md Manjur
A review on recent advances in deep learning for sentiment analysis: Performances, challenges and limitations
description Now days the horizons of social online media keep expanding, the impacts they have on people are huge. For example, many businesses are taking advantage of the input from social media to advertise to specific target market. This is done by detecting and analyzing the sentiment (emotions, feelings, opinions) in social media about any topic or product from the texts. There are numerous machine learning as well as natural language processing methods used to examine public opinions with low time complexity. Deep learning techniques, however, have become widely popular in recent times because of their high efficiency and accuracy. This paper provides a complete overview of the common deep learning frameworks used in sentiment analysis in recent time. We offer a taxonomical study of text representations, learning model, evaluation, metrics and implications of recent advances in deep learning architectures. We also added a special emphasis on deep learning methods; the key findings and limitations of different authors are discussed. This will hopefully help other researchers to do further development of deep learning methods in text processing especially for sentiment analysis. The research also presents the quick summaries of the most popular datasets, lexicons with their related research, performance and main features of the datasets. The aim of this survey is to emphasize the ability to solve text-based sentiment analysis challenges in deep learning architectures with successful achievement for accuracy, speed with context, syntactic and semantic meaning. This review paper analyzes uniquely with the progress and recent advances in sentiment analysis based on existing advanced methods and approach based on deep learning with their findings, performance comparisons and the limitations.
format Article
author Islam, Md Shofiqul
Ngahzaifa, Ab. Ghani
Ahmed, Md Manjur
author_facet Islam, Md Shofiqul
Ngahzaifa, Ab. Ghani
Ahmed, Md Manjur
author_sort Islam, Md Shofiqul
title A review on recent advances in deep learning for sentiment analysis: Performances, challenges and limitations
title_short A review on recent advances in deep learning for sentiment analysis: Performances, challenges and limitations
title_full A review on recent advances in deep learning for sentiment analysis: Performances, challenges and limitations
title_fullStr A review on recent advances in deep learning for sentiment analysis: Performances, challenges and limitations
title_full_unstemmed A review on recent advances in deep learning for sentiment analysis: Performances, challenges and limitations
title_sort review on recent advances in deep learning for sentiment analysis: performances, challenges and limitations
publisher COMPUSOFT
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/28974/1/A%20review%20on%20recent%20advances%20in%20deep%20learning.pdf
http://umpir.ump.edu.my/id/eprint/28974/
https://ijact.in/index.php/ijact/article/view/1175
_version_ 1675327475481051136
score 13.211869

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