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"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"

Social media is used to categorise products or services, but analysing vast comments is time-consuming. Researchers use sentiment analysis via natural language processing, evaluating methods and results conventionally through literature reviews and assessments. However, our approach diverges by offe...

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Main Authors: Islam, Md Shofiqul, Kabir, Muhammad Nomani, Ngahzaifa, Ab Ghani, Kamal Zuhairi, Zamli, Nor Saradatul Akmar, Zulkifli, Rahman, Md Mustafizur, Moni, Mohammad Ali
Format: Article
Language:English
English
Published: Springer Nature 2024
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
Online Access:http://umpir.ump.edu.my/id/eprint/41421/1/Challenges%20and%20future%20in%20deep%20learning%20for%20sentiment%20analysis.pdf
http://umpir.ump.edu.my/id/eprint/41421/2/Challenges%20and%20future%20in%20deep%20learning%20for%20sentiment%20analysis_A%20comprehensive%20review%20and%20a%20proposed%20novel%20hybrid%20approach_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41421/
https://doi.org/10.1007/s10462-023-10651-9
https://doi.org/10.1007/s10462-023-10651-9
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spelling my.ump.umpir.414212024-07-31T00:43:13Z http://umpir.ump.edu.my/id/eprint/41421/ "Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach" Islam, Md Shofiqul Kabir, Muhammad Nomani Ngahzaifa, Ab Ghani Kamal Zuhairi, Zamli Nor Saradatul Akmar, Zulkifli Rahman, Md Mustafizur Moni, Mohammad Ali QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Social media is used to categorise products or services, but analysing vast comments is time-consuming. Researchers use sentiment analysis via natural language processing, evaluating methods and results conventionally through literature reviews and assessments. However, our approach diverges by offering a thorough analytical perspective with critical analysis, research findings, identified gaps, limitations, challenges and future prospects specific to deep learning-based sentiment analysis in recent times. Furthermore, we provide in-depth investigation into sentiment analysis, categorizing prevalent data, pre-processing methods, text representations, learning models, and applications. We conduct a thorough evaluation of recent advances in deep learning architectures, assessing their pros and cons. Additionally, we offer a meticulous analysis of deep learning methodologies, integrating insights on applied tools, strengths, weaknesses, performance results, research gaps, and a detailed feature-based examination. Furthermore, we present in a thorough discussion of the challenges, drawbacks, and factors contributing to the successful enhancement of accuracy within the realm of sentiment analysis. A critical comparative analysis of our article clearly shows that capsule-based RNN approaches give the best results with an accuracy of 98.02% which is the CNN or RNN-based models. We implemented various advanced deep-learning models across four benchmarks to identify the top performers. Additionally, we introduced the innovative CRDC (Capsule with Deep CNN and Bi structured RNN) model, which demonstrated superior performance compared to other methods. Our proposed approach achieved remarkable accuracy across different databases: IMDB (88.15%), Toxic (98.28%), CrowdFlower (92.34%), and ER (95.48%). Hence, this method holds promise for automated sentiment analysis and potential deployment. Springer Nature 2024-03 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41421/1/Challenges%20and%20future%20in%20deep%20learning%20for%20sentiment%20analysis.pdf pdf en http://umpir.ump.edu.my/id/eprint/41421/2/Challenges%20and%20future%20in%20deep%20learning%20for%20sentiment%20analysis_A%20comprehensive%20review%20and%20a%20proposed%20novel%20hybrid%20approach_ABS.pdf Islam, Md Shofiqul and Kabir, Muhammad Nomani and Ngahzaifa, Ab Ghani and Kamal Zuhairi, Zamli and Nor Saradatul Akmar, Zulkifli and Rahman, Md Mustafizur and Moni, Mohammad Ali (2024) "Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach". Artificial Intelligence Review, 57 (62). pp. 1-79. ISSN 0269-2821. (Published) https://doi.org/10.1007/s10462-023-10651-9 https://doi.org/10.1007/s10462-023-10651-9
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
Islam, Md Shofiqul
Kabir, Muhammad Nomani
Ngahzaifa, Ab Ghani
Kamal Zuhairi, Zamli
Nor Saradatul Akmar, Zulkifli
Rahman, Md Mustafizur
Moni, Mohammad Ali
"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"
description Social media is used to categorise products or services, but analysing vast comments is time-consuming. Researchers use sentiment analysis via natural language processing, evaluating methods and results conventionally through literature reviews and assessments. However, our approach diverges by offering a thorough analytical perspective with critical analysis, research findings, identified gaps, limitations, challenges and future prospects specific to deep learning-based sentiment analysis in recent times. Furthermore, we provide in-depth investigation into sentiment analysis, categorizing prevalent data, pre-processing methods, text representations, learning models, and applications. We conduct a thorough evaluation of recent advances in deep learning architectures, assessing their pros and cons. Additionally, we offer a meticulous analysis of deep learning methodologies, integrating insights on applied tools, strengths, weaknesses, performance results, research gaps, and a detailed feature-based examination. Furthermore, we present in a thorough discussion of the challenges, drawbacks, and factors contributing to the successful enhancement of accuracy within the realm of sentiment analysis. A critical comparative analysis of our article clearly shows that capsule-based RNN approaches give the best results with an accuracy of 98.02% which is the CNN or RNN-based models. We implemented various advanced deep-learning models across four benchmarks to identify the top performers. Additionally, we introduced the innovative CRDC (Capsule with Deep CNN and Bi structured RNN) model, which demonstrated superior performance compared to other methods. Our proposed approach achieved remarkable accuracy across different databases: IMDB (88.15%), Toxic (98.28%), CrowdFlower (92.34%), and ER (95.48%). Hence, this method holds promise for automated sentiment analysis and potential deployment.
format Article
author Islam, Md Shofiqul
Kabir, Muhammad Nomani
Ngahzaifa, Ab Ghani
Kamal Zuhairi, Zamli
Nor Saradatul Akmar, Zulkifli
Rahman, Md Mustafizur
Moni, Mohammad Ali
author_facet Islam, Md Shofiqul
Kabir, Muhammad Nomani
Ngahzaifa, Ab Ghani
Kamal Zuhairi, Zamli
Nor Saradatul Akmar, Zulkifli
Rahman, Md Mustafizur
Moni, Mohammad Ali
author_sort Islam, Md Shofiqul
title "Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"
title_short "Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"
title_full "Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"
title_fullStr "Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"
title_full_unstemmed "Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"
title_sort "challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"
publisher Springer Nature
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41421/1/Challenges%20and%20future%20in%20deep%20learning%20for%20sentiment%20analysis.pdf
http://umpir.ump.edu.my/id/eprint/41421/2/Challenges%20and%20future%20in%20deep%20learning%20for%20sentiment%20analysis_A%20comprehensive%20review%20and%20a%20proposed%20novel%20hybrid%20approach_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41421/
https://doi.org/10.1007/s10462-023-10651-9
https://doi.org/10.1007/s10462-023-10651-9
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score 13.232414

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