Evolving techniques in sentiment analysis: a comprehensive review
With the rapid expansion of social media and e-commerce platforms, an unprecedented volume of user-generated content has emerged, offering organizations, governments, and researchers invaluable insights into public sentiment. Yet, the vast and unstructured nature of this data challenges traditional...
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2592.pdf |
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| author | Mahander Kumar Lal Khan Hsien-Tsung Chang |
| author_facet | Mahander Kumar Lal Khan Hsien-Tsung Chang |
| author_sort | Mahander Kumar |
| collection | DOAJ |
| description | With the rapid expansion of social media and e-commerce platforms, an unprecedented volume of user-generated content has emerged, offering organizations, governments, and researchers invaluable insights into public sentiment. Yet, the vast and unstructured nature of this data challenges traditional analysis methods. Sentiment analysis, a specialized field within natural language processing, has evolved to meet these challenges by automating the detection and categorization of opinions and emotions in text. This review comprehensively examines the evolving techniques in sentiment analysis, detailing foundational processes such as data gathering and feature extraction. It explores a spectrum of methodologies, from classical word embedding techniques and machine learning algorithms to recent contextual embedding and advanced transformer models like Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), and T5. With a critical comparison of these methods, this article highlights their appropriate uses and limitations. Additionally, the review provides a thorough overview of current trends, insights into future directions, and a critical exploration of unresolved challenges. By synthesizing these developments, this review equips researchers with a solid foundation for assessing the current state of sentiment analysis and guiding future advancements in this dynamic field. |
| format | Article |
| id | doaj-art-be41626592a8429b875cb496c8ea9006 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-be41626592a8429b875cb496c8ea90062025-01-30T15:05:13ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e259210.7717/peerj-cs.2592Evolving techniques in sentiment analysis: a comprehensive reviewMahander Kumar0Lal Khan1Hsien-Tsung Chang2Department of Computer Science, Mir Chakar Khan Rind University, Sibi, Balochistan, PakistanDepartment of Computer Science, IBADAT Internationl University Islamabad, Pakpattan Campus, PakistanDepartment of Computer Science and Information Engineering, Chang Gung University, Taoyuan, TaiwanWith the rapid expansion of social media and e-commerce platforms, an unprecedented volume of user-generated content has emerged, offering organizations, governments, and researchers invaluable insights into public sentiment. Yet, the vast and unstructured nature of this data challenges traditional analysis methods. Sentiment analysis, a specialized field within natural language processing, has evolved to meet these challenges by automating the detection and categorization of opinions and emotions in text. This review comprehensively examines the evolving techniques in sentiment analysis, detailing foundational processes such as data gathering and feature extraction. It explores a spectrum of methodologies, from classical word embedding techniques and machine learning algorithms to recent contextual embedding and advanced transformer models like Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), and T5. With a critical comparison of these methods, this article highlights their appropriate uses and limitations. Additionally, the review provides a thorough overview of current trends, insights into future directions, and a critical exploration of unresolved challenges. By synthesizing these developments, this review equips researchers with a solid foundation for assessing the current state of sentiment analysis and guiding future advancements in this dynamic field.https://peerj.com/articles/cs-2592.pdfSentiment analysisNatural language processingSocial media |
| spellingShingle | Mahander Kumar Lal Khan Hsien-Tsung Chang Evolving techniques in sentiment analysis: a comprehensive review PeerJ Computer Science Sentiment analysis Natural language processing Social media |
| title | Evolving techniques in sentiment analysis: a comprehensive review |
| title_full | Evolving techniques in sentiment analysis: a comprehensive review |
| title_fullStr | Evolving techniques in sentiment analysis: a comprehensive review |
| title_full_unstemmed | Evolving techniques in sentiment analysis: a comprehensive review |
| title_short | Evolving techniques in sentiment analysis: a comprehensive review |
| title_sort | evolving techniques in sentiment analysis a comprehensive review |
| topic | Sentiment analysis Natural language processing Social media |
| url | https://peerj.com/articles/cs-2592.pdf |
| work_keys_str_mv | AT mahanderkumar evolvingtechniquesinsentimentanalysisacomprehensivereview AT lalkhan evolvingtechniquesinsentimentanalysisacomprehensivereview AT hsientsungchang evolvingtechniquesinsentimentanalysisacomprehensivereview |