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research-article

Systematic reviews in sentiment analysis: a tertiary study

Authors:

Alexander Ligthart,

Bedir TekinerdoganAuthors Info & Claims

Artificial Intelligence Review, Volume 54, Issue 7

Pages 4997 - 5053

https://doi.org/10.1007/s10462-021-09973-3

Published: 01 October 2021 Publication History

Abstract

With advanced digitalisation, we can observe a massive increase of user-generated content on the web that provides opinions of people on different subjects. Sentiment analysis is the computational study of analysing people's feelings and opinions for an entity. The field of sentiment analysis has been the topic of extensive research in the past decades. In this paper, we present the results of a tertiary study, which aims to investigate the current state of the research in this field by synthesizing the results of published secondary studies (i.e., systematic literature review and systematic mapping study) on sentiment analysis. This tertiary study follows the guidelines of systematic literature reviews (SLR) and covers only secondary studies. The outcome of this tertiary study provides a comprehensive overview of the key topics and the different approaches for a variety of tasks in sentiment analysis. Different features, algorithms, and datasets used in sentiment analysis models are mapped. Challenges and open problems are identified that can help to identify points that require research efforts in sentiment analysis. In addition to the tertiary study, we also identified recent 112 deep learning-based sentiment analysis papers and categorized them based on the applied deep learning algorithms. According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis.

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Cited By

Lee JChen SMordahl ALiu CYang WWei S(2024)Automated Testing Linguistic Capabilities of NLP ModelsACM Transactions on Software Engineering and Methodology10.1145/367245533:7(1-33)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3672455
Diwali ASaeedi KDashtipour KGogate MCambria EHussain A(2024)Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2023.329637315:3(837-846)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TAFFC.2023.3296373
Hasib KNaseem UKeya AMaitra SMithu KAlam M(2024)Systematic Literature Review on Sentiment Analysis in Airline IndustrySN Computer Science10.1007/s42979-024-03567-w6:1Online publication date: 23-Dec-2024
https://dl.acm.org/doi/10.1007/s42979-024-03567-w
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Index Terms

Systematic reviews in sentiment analysis: a tertiary study
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis

Index terms have been assigned to the content through auto-classification.

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cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 54, Issue 7

Oct 2021

795 pages

ISSN:0269-2821

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© The Author(s) 2021.

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Kluwer Academic Publishers

United States

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Published: 01 October 2021

Accepted: 08 February 2021

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Cited By

Lee JChen SMordahl ALiu CYang WWei S(2024)Automated Testing Linguistic Capabilities of NLP ModelsACM Transactions on Software Engineering and Methodology10.1145/367245533:7(1-33)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3672455
Diwali ASaeedi KDashtipour KGogate MCambria EHussain A(2024)Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2023.329637315:3(837-846)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TAFFC.2023.3296373
Hasib KNaseem UKeya AMaitra SMithu KAlam M(2024)Systematic Literature Review on Sentiment Analysis in Airline IndustrySN Computer Science10.1007/s42979-024-03567-w6:1Online publication date: 23-Dec-2024
https://dl.acm.org/doi/10.1007/s42979-024-03567-w
Kowsik VYashwanth LHarish SKishore AS RJose AV D(2024)Sentiment analysis of twitter data to detect and predict political leniency using natural language processingJournal of Intelligent Information Systems10.1007/s10844-024-00842-362:3(765-785)Online publication date: 1-Jun-2024
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Zou HWang Y(2024)A novel automated framework for fine-grained sentiment analysis of application reviews using deep neural networksAutomated Software Engineering10.1007/s10515-024-00444-x31:2Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1007/s10515-024-00444-x
singh TKumari MGupta D(2024)Context-Based Persuasion Analysis of Sentiment Polarity Disambiguation in Social Media Text StreamsNew Generation Computing10.1007/s00354-023-00238-x42:4(497-531)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s00354-023-00238-x
Toldy FBelo O(2024)An Ontology-Lexicon-Driven Approach for Refining Sentiment Analysis ProcessesIntelligent Data Engineering and Automated Learning – IDEAL 202410.1007/978-3-031-77738-7_5(46-57)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-77738-7_5
Ibrohim MBosco CBasile V(2023)Sentiment Analysis for the Natural Environment: A Systematic ReviewACM Computing Surveys10.1145/360460556:4(1-37)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3604605
Matrane YBenabbou FSael N(2023)A systematic literature review of Arabic dialect sentiment analysisJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10157035:6Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.jksuci.2023.101570
Ermakova TFabian BGolimblevskaia EHenke M(2023)A Comparison of Commercial Sentiment Analysis ServicesSN Computer Science10.1007/s42979-023-01886-y4:5Online publication date: 24-Jun-2023
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