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Trends and challenges in sentiment summarization: a systematic review of aspect extraction techniques

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Abstract

Sentiment Summarization is an automated technology that extracts important features of sentences and then reorganizes selected words or sentences by their aspect class and sentiment polarity. This emerging research area wields considerable influence, where a sentiment-based summary can provide insight into users’ subjective opinions, creating social engagement that benefits industry players and entrepreneurs. Meanwhile, systematic studies examining sentiment-based summarization, particularly those delving into aspect levels, are still limited. Whereas aspects are crucial to obtain a comprehensive assessment of a product or service for improving sentiment summarization results. Hence, we conducted a comprehensive survey of aspect extraction techniques in sentiment summarization by classifying techniques based on sentiment analysis levels and features. This work analyzes the current research trends and challenges in the research domain from a different perspective. More than 150 literature published from 2004 to 2023 are collected mainly from credible academic databases. We summarized and performed a comparative analysis of the sentiment summarization approaches and tabulated their performance based on different domains, sentiment levels, and features. We also derived a thematic taxonomy of aspect extraction techniques in sentiment summarization from the analysis and illustrated its usage in various applications. Finally, this study presents recommendations for the challenges and opportunities for future research development.

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References

  1. Statista (2021) Total number of user reviews and opinions on Tripadvisor worldwide from 2014 to 2020 (in millions). Statista Research Department, 2021. https://www.statista.com/statistics/684862/tripadvisor-number-of-reviews/. Accessed 13 Nov 2021.

  2. Dixon S (2022) How do online customer reviews affect your opinion of a local business?. https://www.statista.com/statistics/315751/online-review-customer-opinion/. Accessed 28 Dec 2022

  3. Lloret E, Palomar M (2011) Text summarisation in progress: a literature review. Artif Intell Rev 37(1):1–41

    Article  Google Scholar 

  4. Liu B (2012) Sentiment analysis and sentiment analysis and opinion mining. Morgan & Claypool, San Rafael

    Book  Google Scholar 

  5. Moussa ME, Mohamed EH, Haggag MH (2018) A survey on opinion summarization techniques for social media. Future Comput Inform J 3(1):82–109

    Article  Google Scholar 

  6. Beineke P, Hastie T, Manning C, Vaithyanathan S (2004) Exploring sentiment summarization. In: AAAI spring symposium—technical report, 2004, vol SS-04-07, pp 12–15

  7. Mukherjee A, Liu B (2012) Aspect extraction through semi-supervised modeling. In: 50th annual meeting of the association for computational linguistics, ACL 2012—proceedings of the conference, 2012, vol 1, no. July, pp 339–348

  8. Das SJ, Murakami R, Chakraborty B (2021) Development of a two-step LDA based aspect extraction technique for review summarization. Int J Appl Sci Eng 18(1):1–18

    Google Scholar 

  9. Kim H, Ganesan K (2011) Comprehensive review of opinion summarization. Illinois Environ 1–30

  10. Giachanou A, Crestani F (2016) Like it or not: a survey of twitter sentiment analysis methods. ACM Comput Surv 49(2):1–41

    Article  Google Scholar 

  11. Tubishat M, Idris N, Abushariah MAM (2018) Implicit aspect extraction in sentiment analysis: review, taxonomy, oppportunities, and open challenges. Inf Process Manag 54(4):545–563

    Article  Google Scholar 

  12. Yue L, Chen W, Li X, Zuo W, Yin M (2019) A survey of sentiment analysis in social media. Knowl Inf Syst 60(2):617–663

    Article  Google Scholar 

  13. Nazir A, Rao Y, Wu L, Sun L (2020) Issues and challenges of aspect-based sentiment analysis: a comprehensive survey. IEEE Trans Affect Comput 13(2):845–863

    Article  Google Scholar 

  14. Cai H, Xia R, Yu J (2021) Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions. In: Proceedings ofthe 59th annual meeting ofthe association for computational linguistics and the 11th international joint conference on natural language processing, pp 340–350

  15. Komwad N, Tiwari P, Praveen B, Chowdary CR (2022) A survey on review summarization and sentiment classification. Knowl Inf Syst 64(9):2289–2327

    Article  Google Scholar 

  16. Maitama JZ, Idris N, Abdi A, Shuib L, Fauzi R (2020) A systematic review on implicit and explicit aspect extraction in sentiment analysis. IEEE Access 8(November):194166–194191

    Article  Google Scholar 

  17. Mahajani A, Pandya V, Maria I, Sharma D (2019) A comprehensive survey on extractive and abstractive techniques for text summarization. Adv Intell Syst Comput 904:339–351

    Article  Google Scholar 

  18. Nenkova A, McKeown K (2011) Automatic summarization. Found Trends Inf Retr 5(2–3):103–233

    Article  Google Scholar 

  19. Hayatin N, Alias S, Hung LP, Sainin MS (2022) Sentiment analysis based on probabilistic classifier techniques in various indonesian review data. Jordanian J Comput Inf Technol 08(3):271–282

    Google Scholar 

  20. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113

    Article  Google Scholar 

  21. Yi J, Nasukawa T, Bunescu R, Niblack W (2003) Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In: Proceedings—IEEE international conference on data mining, iCDM, 2003, pp 427–434

  22. Hu YH, Chen YL, Chou HL (2017) Opinion mining from online hotel reviews—a text summarization approach. Inf Process Manag 53(2):436–449

    Article  Google Scholar 

  23. Hussain SF, Babar HZUD, Khalil A, Jillani RM, Hanif M, Khurshid K (2020) A fast non-redundant feature selection technique for text data. IEEE Access 8:181763–181781

    Article  Google Scholar 

  24. Thakkar HK, Sahoo PK, Mohanty P (2021) DOFM: domain feature miner for robust extractive summarization. Inf Process Manag 58(3):102474

    Article  Google Scholar 

  25. Tan B, Qin L, Xing E, Hu Z (2020) Summarizing text on any aspects: a knowledge-informed weakly-supervised approach. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), 2020, pp 6301–6309

  26. Huang S-L, Cheng W-C (2015) Discovering Chinese sentence patterns for feature-based opinion summarization. Electron Commer Res Appl 14(6):582–591

    Article  Google Scholar 

  27. Jiang W, Chen J, Ding X (2021) Review summary generation in online systems: frameworks for supervised and unsupervised scenarios. ACM Trans Web 15(3):1–33

    Article  Google Scholar 

  28. López M, Martínez-Cámara E, Luzón MV, Herrera F (2021) ADOPS: aspect discovery opinion summarisation methodology based on deep learning and subgroup discovery for generating explainable opinion summaries. Knowl-Based Syst 231:107455

    Article  Google Scholar 

  29. Kumar A, Seth S, Gupta S, Maini S (2021) Sentic computing for aspect-based opinion summarization using multi-head attention with feature pooled pointer generator network. Cognit Comput 14(1):130–148

    Article  Google Scholar 

  30. Kitchenham B, Pearl Brereton O, Budgen D, Turner M, Bailey J, Linkman S (2009) Systematic literature reviews in software engineering—a systematic literature review. Inf Softw Technol 51(1):7–15

    Article  Google Scholar 

  31. Page MJ et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg 88:105906

    Article  Google Scholar 

  32. Ataei TS, Darvishi K, Javdan S, Minaei-Bidgoli B, Eetemadi S (2019) Pars-ABSA: an aspect-based sentiment analysis dataset for Persian, pp 1–6

  33. Huang J, Xue Y, Hu X, Jin H, Lu X, Liu Z (2019) Sentiment analysis of Chinese online reviews using ensemble learning framework. Cluster Comput 22:3043–3058

    Article  Google Scholar 

  34. Alturaief N, Aljamaan H, Baslyman M (2021) AWARE: aspect-based sentiment analysis dataset of apps reviews for requirements elicitation. In: Proc. - 2021 36th IEEE/ACM int. conf. autom. softw. eng. work. ASEW 2021, pp 211–218

  35. Mudalige CR et al (2020) SigmaLaw-ABSA: dataset for aspect-based sentiment analysis in legal opinion texts. In: 2020 IEEE 15th int. conf. ind. inf. syst. ICIIS 2020—proc., pp 488–493

  36. Angelidis S, Amplayo RK, Suhara Y, Wang X, Lapata M (2021) Extractive opinion summarization in quantized transformer spaces. Trans Assoc Comput Linguist 9:277–293

    Article  Google Scholar 

  37. Nguyen Ngoc D, Phan Thi T, Do P (2019) A data preprocessing method to classify and summarize aspect-based opinions using deep learning. In: Lect. notes comput. sci. (including subser. lect. notes artif. intell. lect. notes bioinformatics), vol 11431 LNAI, no. December, pp 115–127

  38. Kang Y, Zhou L (2017) RubE: rule-based methods for extracting product features from online consumer reviews. Inf Manag 54(2):166–176

    Article  Google Scholar 

  39. Nurrahmi H, Maharani W, Saadah S (2016) Feature extraction and opinion classification using class sequential rule on customer product review. In: 2016 4th int. conf. inf. commun. technol. ICoICT 2016

  40. Ku L, Lee L-Y, Wu T-H, Chen HH (2005) Major topic detection and its application to opinion summarization. In: SIGIR 2005—proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, 2005, pp 627–628

  41. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: KDD-2004—proc. tenth ACM SIGKDD int. conf. knowl. discov. data min., pp 168–177

  42. Rana TA, Cheah Y-N, Rana T (2020) Multi-level knowledge-based approach for implicit aspect identification. Appl Intell 50(12):4616–4630

    Article  Google Scholar 

  43. Xu Q, Zhu L, Dai T, Guo L, Cao S (2020) Non-negative matrix factorization for implicit aspect identification. J Ambient Intell Humaniz Comput 11(7):2683–2699

    Article  Google Scholar 

  44. Liu B (2012) Sentiment analysis and opinion mining

  45. Peal M, Hossain MS, Chen J (2022) Summarizing consumer reviews. J Intell Inf Syst 59:193–212

    Article  Google Scholar 

  46. Hu Y (2017) Opinion mining from online hotel reviews—a text summarization approach. Inf Process Manag 53(2):436–449

    Article  Google Scholar 

  47. Ma Y, Li Q (2019) A weakly-supervised extractive framework for sentiment-preserving document summarization. World Wide Web 22(4):1401–1425

    Article  Google Scholar 

  48. Marzijarani SB, Sajedi H (2020) Opinion mining with reviews summarization based on clustering. Int J Inf Technol 12(4):1299–1310

    Google Scholar 

  49. Abdi A, Shamsuddin SM, Hasan S, Piran J (2019) Automatic sentiment-oriented summarization of multi-documents using soft computing. Soft Comput 23(20):10551–10568

    Article  Google Scholar 

  50. Abdi A, Hasan S, Shamsuddin SM, Idris N, Piran J (2021) A hybrid deep learning architecture for opinion-oriented multi-document summarization based on multi-feature fusion. Knowl-Based Syst 213:106658

    Article  Google Scholar 

  51. Tsai CF, Chen K, Hu YH, Chen WK (2020) Improving text summarization of online hotel reviews with review helpfulness and sentiment. Tour Manag 80:104122

    Article  Google Scholar 

  52. Abdi A, Shamsuddin SM, Hasan S, Piran J (2018) Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment. Expert Syst Appl 109:66–85

    Article  Google Scholar 

  53. Zhou X, Wan X, Xiao J (2016) CMiner: opinion extraction and summarization for Chinese microblogs. IEEE Trans Knowl Data Eng 28(7):1650–1663

    Article  Google Scholar 

  54. Nishikawa H, Hasegawa T, Matsuo Y, Kikui G (2010) Opinion summarization with integer linear programming formulation for sentence extraction and ordering. In: Coling 2010—23rd international conference on computational linguistics, proceedings of the conference, 2010, vol 2, pp 910–918

  55. Zhang M, Zhou G, Huang N, He P, Yu W, Liu W (2023) AsU-OSum: aspect-augmented unsupervised opinion summarization. Inf Process Manag 60(1):103138

    Article  Google Scholar 

  56. Guzman E, Ibrahim M, Glinz M (2017) A little bird told me: mining tweets for requirements and software evolution. In: Proc.—2017 IEEE 25th int. requir. eng. conf. RE 2017, no 3, pp 11–20

  57. Uddin G, Khomh F (2017) Automatic summarization of API reviews. IN: ASE 2017—proceedings of the 32nd IEEE/ACM international conference on automated software engineering, pp 159–170

  58. Chu E, Liu PJ (2019) MeanSum: a neural model for unsupervised multi-document abstractive summarization. In: 36th international conference on machine learning, ICML 2019, 2019, vol 2019, pp 2088–2110

  59. Dang HT (2005) Overview of DUC 2005. In: Proc. doc. underst. conf., p. 1Ą12

  60. Zhuang L, Jing F, Zhu X-Y (2006) Movie review mining and summarization. In: International conference on information and knowledge management, proceedings, 2006, pp 43–50

  61. Hayashi H, Budania P, Wang P (2021) WikiAsp: a dataset for multi-domain aspect-based summarization. Trans Assoc Comput Linguist 9:211–225

    Article  Google Scholar 

  62. Ge S, Huang J, Meng Y, Wang S, Han J (2021) Fine-grained opinion summarization with minimal supervision

  63. Marrese-Taylor E, Velásquez JD, Bravo-Marquez F (2014) A novel deterministic approach for aspect-based opinion mining in tourism products reviews. Expert Syst Appl 41(17):7764–7775

    Article  Google Scholar 

  64. Jmal J, Faiz R (2013) Customer review summarization approach using twitter and sentiwordnet. In: ACM international conference proceeding series

  65. Piryani R, Gupta V, Singh VK (2018) Generating aspect-based extractive opinion summary: drawing inferences from social media texts. Comput y Sist 22(1):83–91

    Google Scholar 

  66. He R, Lee WS, Ng HT, Dahlmeier D (2017) An unsupervised neural attention model for aspect extraction. In: ACL 2017—55th annu. meet. assoc. comput. linguist. proc. conf. (Long Pap.), vol 1, pp 388–397

  67. Angelidis S, Lapata M (2018) Summarizing opinions: aspect extraction meets sentiment prediction and they are both weakly supervised. In: Proc. 2018 conf. empir. methods nat. lang. process. EMNLP 2018, vol arXiv, pp 3675–3686

  68. Lloret E, Boldrini E, Vodolazova T, Martínez-Barco P, Muñoz R, Palomar M (2015) A novel concept-level approach for ultra-concise opinion summarization. Expert Syst Appl 42(20):7148–7156

    Article  Google Scholar 

  69. Hu HW, Chen YL, Hsu PT (2016) A novel approach to rate and summarize online reviews according to user-specified aspects. J Electron Commer Res 17(2):132–152

    Google Scholar 

  70. Amplayo RK, Song M (2017) An adaptable fine-grained sentiment analysis for summarization of multiple short online reviews. Data Knowl Eng 110:54–67

    Article  Google Scholar 

  71. Xu X, Meng T, Cheng X (2011) Aspect-based extractive summarization of online reviews. In: Proceedings of the ACM symposium on applied computing, 2011, pp 968–975

  72. Lu Y, Zhai CX, Sundaresan N (2009) Rated aspect summarization of short comments. In: WWW’09—proc. 18th int. world wide web conf., pp 131–140

  73. Zhu JZM, Wang H, Tsou BK (2009) Aspect-based sentence segmentation for sentiment summarization. In: International conference on information and knowledge management, proceedings, 2009, pp 65–72

  74. Blair-Goldensohn S, Hannan K, McDonald R, Neylon T, Reis GA, Reynar J (2008) Building a sentiment summarizer for local service reviews. In: Proceedings of the WWW2008 workshop: NLP in the information explosion era (NLPIX 2008)

  75. Hong M, Wang H (2021) Research on customer opinion summarization using topic mining and deep neural network. Math Comput Simul 185:88–114

    Article  MathSciNet  Google Scholar 

  76. Balahur A, Lloret E, Boldrini E, Montoyo A, Palomar M, Martínez-barco P (2009) Summarizing threads in blogs using opinion polarity. In: Events in emerging text types (eETTs) - Borovets, Bulgaria, 2009, pp 23–31

  77. Stoyanov V, Cardie C (2006) Partially supervised coreference resolution for opinion summarization through structured rule learning. In: COLING/ACL 2006—EMNLP 2006: 2006 conference on empirical methods in natural language processing, proceedings of the conference, 2006, pp 336–344

  78. Amoudi G, Almansour A, Alghamdi HS (2022) Improved graph-based Arabic hotel review summarization using polarity classification. Appl Sci 12(21):10980

    Article  Google Scholar 

  79. Wu P, Li X, Shen S, He D (2020) Social media opinion summarization using emotion cognition and convolutional neural networks. Int J Inf Manag 51(July 2019):101978

    Article  Google Scholar 

  80. Abdi A, Shamsuddin SM, Aliguliyev RM (2018) QMOS: query-based multi-documents opinion-oriented summarization. Inf Process Manag 54(2):318–338

    Article  Google Scholar 

  81. Balahur A, Kabadjov M, Steinberger J, Steinberger R, Montoyo A (2012) Challenges and solutions in the opinion summarization of user-generated content. J Intell Inf Syst 39(2):375–398

    Article  Google Scholar 

  82. Kokkoras F, Ntonas ELK, Vlahavas I (2008) MOpiS: a multiple opinion summarizer. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 5138, pp 110–122

  83. Darmawiguna IGM, Pradnyana GA, Jyotisananda IB (1810) Indonesian sentiment summarization for lecturer learning evaluation by using textrank algorithm. J Phys Conf Ser 1:2021

    Google Scholar 

  84. Sheikh AA, Arif T, Malik MB, Bhat SI (2021) Extraction and summarization of reviews using lexicon based approach. IOP Conf Ser Mater Sci Eng 1022(1):0–7

    Article  Google Scholar 

  85. Ali SM, Noorian Z, Bagheri E, Ding C, Al-Obeidat F (2018) Topic and sentiment aware microblog summarization for twitter. J Intell Inf Syst 54(1):129–156

    Article  Google Scholar 

  86. Dadhich A, Thankachan B (2021) Social and juristic challenges of AI for opinion mining approaches on Amazon and flipkart product reviews using machine learning algorithms. SN Comput Sci 2(3):180

    Article  Google Scholar 

  87. Hou T, Yannou B, Leroy Y, Poirson E (2019) Mining customer product reviews for product development: a summarization process. Expert Syst Appl 132:141–150

    Article  Google Scholar 

  88. Wang WM, Li Z, Tian ZG, Wang JW, Cheng MN (2018) Extracting and summarizing affective features and responses from online product descriptions and reviews: a Kansei text mining approach. Eng Appl Artif Intell 73(October 2017):149–162

    Article  Google Scholar 

  89. Zhang R, Yu W, Sha C, He X, Zhou A (2015) Product-oriented review summarization and scoring. Front Comput Sci 9(2):210–223

    Article  MathSciNet  Google Scholar 

  90. Wang D, Zhu S, Li T (2013) SumView: a web-based engine for summarizing product reviews and customer opinions. Expert Syst Appl 40(1):27–33

    Article  Google Scholar 

  91. Di Fabbrizio G, Aker A, Gaizauskas R (2013) Summarizing online reviews using aspect rating distributions and language modeling. IEEE Intell Syst 28(3):28–37

    Article  Google Scholar 

  92. Xu K, Liao SS, Li J, Song Y (2011) Mining comparative opinions from customer reviews for Competitive Intelligence. Decis Support Syst 50(4):743–754

    Article  Google Scholar 

  93. Li Q, Jin Z, Wang C, Zeng DD (2016) Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems. Knowl-Based Syst 107:289–300

    Article  Google Scholar 

  94. Valarmathi B, Palanisamy V (2011) Opinion mining classification using key word summarization based on singular value decomposition. Int J Comput Sci Eng 3(1):212–215

    Google Scholar 

  95. Zheng Y, Li X, Su G, Ma J, Ning C (2020) Position-aware hybrid attention network for aspect-level sentiment analysis. In: Lect. notes comput. sci. (including subser. lect. notes artif. intell. lect. notes bioinformatics), vol 12285 LNCS, no 2, pp 83–95

  96. Maulidiah Elfajr N, Sarno R (2018) Sentiment analysis using weighted emoticons and SentiWordNet for Indonesian language. In: Proceedings - 2018 international seminar on application for technology of information and communication: creative technology for human life, iSemantic 2018, pp 234–238

  97. Marstawi A, Sharef NM, Aris TNM, Mustapha A (2017) Ontology-based aspect extraction for an improved sentiment analysis in summarization of product reviews. In: Proceedings of the 8th international conference on computer modeling and simulation, {ICCMS} 2017. Canberra, Australia, January 20–23, 20172017, pp 100–104

  98. Lloret E, Balahur A, Gómez JM, Montoyo A, Palomar M (2012) Towards a unified framework for opinion retrieval, mining and summarization. J Intell Inf Syst 39(3):711–747

    Article  Google Scholar 

  99. Meng X, Wei F, Liu X, Zhou M, Li S, Wang H (2012) Entity-centric topic-oriented opinion summarization in twitter. In: Proc. ACM SIGKDD int. conf. knowl. discov. data min., pp 379–387

  100. Sarvabhotla K, Pingali P, Varma V (2011) Sentiment classification: a lexical similarity based approach for extracting subjectivity in documents. Inf Retr 14(3):337–353

    Article  Google Scholar 

  101. Weng J, Yang C-L, Chen B-N, Wang Y-K, Lin S-D (2011) IMASS: an intelligent Microblog analysis and summarization system. In: ACL HLT 2011—49th annual meeting of the association for computational linguistics: human language technologies, proceedings of student session, pp 133–138

  102. Gerani S, Carenini G, Ng RT (2019) Modeling content and structure for abstractive review summarization. Comput Speech Lang 53:302–331

    Article  Google Scholar 

  103. Rakesh V, Ding W, Ahuja A, Rao N, Sun Y, Reddy CK (2018) A sparse topic model for extracting aspect-specific summaries from online reviews. In: Web conf. 2018—proc. world wide web conf. WWW 2018, pp 1573–1582

  104. Wu H, Gu Y, Sun S, Gu X (2016) Aspect-based opinion summarization with convolutional neural networks. In: Proc. int. jt. conf. neural networks, vol 2016-Octob, pp 3157–3163

  105. Gu X, Gu Y, Wu H (2017) Cascaded convolutional neural networks for aspect-based opinion summary. Neural Process Lett 46(2):581–594

    Article  Google Scholar 

  106. Nguyen HT, Le T, Le Nguyen M (2019) Opinions summarization: aspect similarity recognition relaxes the constraint of predefined aspects. In: Int. conf. recent adv. nat. lang. process. RANLP, vol 2019-Septe, pp 487–496

  107. Hu M, Liu B (2004) Mining opinion features in customer reviews. In: Proceedings of the national conference on artificial intelligence, 2004, pp 755–760

  108. Shimada K, Tadano R, Endo T (2011) Multi-aspects review summarization with objective information. Procedia Soc Behav Sci 27:140–149

    Article  Google Scholar 

  109. Titov I, McDonald R (2008) A joint model of text and aspect ratings for sentiment summarization. In: ACL-08: HLT—46th annual meeting of the association for computational linguistics: human language technologies, proceedings of the conference, pp 308–316

  110. Amarouche K, Benbrahim H, Kassou I (2018) Customer product review summarization over time for competitive intelligence. J Autom Mob Robot Intell Syst 12(4):70–82

    Google Scholar 

  111. Tadano R, Shimada K, Endo T (2010) Multi-aspects review summarization based on identification of important opinions and their similarity. In: PACLIC 24—proceedings of the 24th Pacific Asia conference on language, information and computation, 2010, no 2008, pp 685–692

  112. Saeed RMK, Rady S, Gharib TF (2022) An ensemble approach for spam detection in Arabic opinion texts. J King Saud Univ Comput Inf Sci 34(1):1407–1416

    Google Scholar 

  113. Chamid AA (2023) Graph-based semi-supervised deep learning for Indonesian aspect-based sentiment analysis

  114. Khan MR, Kannan R (2017) Extracting sentiments and summarizing health reviews from social media using machine learning techniques. Trans Mach Learn Artif Intell 6(1):24

    Google Scholar 

  115. Mane VL, Panicker SS, Patil VB (2015) Summarization and sentiment analysis from user health posts. In: 2015 international conference on pervasive computing: advance communication technology and application for society, ICPC 2015

  116. Liang J, Bao J, Wang Y, Wu Y, He X, Zhou B (2021) CUSTOM: aspect-oriented product summarization for E-commerce. In: International conference on natural language processing and chinese computing, 2021, pp 124–136

  117. Li Y (2013) Deriving market intelligence from microblogs. Decis Support Syst 55(1):206–217

    Article  Google Scholar 

  118. Li H, Wang Y, Mou X, Peng Q (2020) Sentiment classification of financial microblogs through automatic text summarization. In: Proc.—2020 Chinese autom. congr. CAC 2020, pp 5579–5584

  119. Huang Y, Yu Z, Guo J, Yu Z, Xian Y (2020) Legal public opinion news abstractive summarization by incorporating topic information. Int J Mach Learn Cybern 11(9):2039–2050

    Article  Google Scholar 

  120. Dong R, O’Mahony MP, Schaal M, McCarthy K, Smyth B (2016) Combining similarity and sentiment in opinion mining for product recommendation. J Intell Inf Syst 46(2):285–312

    Article  Google Scholar 

  121. Bai P, Xia Y, Xia Y (2020) Fusing knowledge and aspect sentiment for explainable recommendation. IEEE Access 8:137150–137160

    Article  Google Scholar 

  122. Ouyang Y (2017) SentiStory: multi-grained sentiment analysis and event summarization with crowdsourced social media data. Pers Ubiquitous Comput 21(1):97–111

    Article  Google Scholar 

  123. Bražinskas A (2022) Low- and high-resource opinion summarization

  124. Mukherjee R, Peruri HC, Vishnu U, Goyal P, Bhattacharya S, Ganguly N (2020) Read what you need: controllable aspect-based opinion summarization of tourist reviews. In: SIGIR 2020—proc. 43rd int. ACM SIGIR conf. res. dev. inf. retr., pp 1825–1828

  125. Siledar T, Makwana J, Bhattacharyya P (2023) Aspect-sentiment-based opinion summarization using multiple information sources. In: ACM int. conf. proceeding ser., pp 55–61

  126. Ouerhani N, Maalel A, Ben Ghézala H (2022) SMAD: SMart assistant during and after a medical emergency case based on deep learning sentiment analysis: the pandemic COVID-19 case. Cluster Comput 25(5):3671–3681

    Article  Google Scholar 

  127. Yadav A, Patel A, Shah M (2021) A comprehensive review on resolving ambiguities in natural language processing. AI Open 2(July):85–92

    Article  Google Scholar 

  128. Eke CI, Norman AA, Shuib L, Nweke HF (2020) Sarcasm identification in textual data: systematic review, research challenges and open directions. Artif Intell Rev 53(6):4215–4258

    Article  Google Scholar 

  129. Carenini G (2008) Summarizing emails with conversational cohesion and subjectivity. In: ACL-08: HLT—46th annual meeting of the association for computational linguistics: human language technologies, proceedings of the conference, pp 353–361

  130. Chaturvedi I, Cambria E, Welsch RE, Herrera F (2018) Distinguishing between facts and opinions for sentiment analysis: survey and challenges. Inf Fusion 44:65–77

    Article  Google Scholar 

  131. Kansal H, Toshniwal D (2014) Aspect based summarization of context dependent opinion words. Procedia Comput Sci 35:166–175

    Article  Google Scholar 

  132. Cambria E, Hussain A, Durrani T, Havasi C, Eckl C, Munro J (2010) Sentic computing for patient centered applications. In: International conference on signal processing proceedings, ICSP, 2010, pp 1279–1282

  133. Verma K, Davis B (2021) Implicit aspect-based opinion mining and analysis of airline industry based on user-generated reviews. SN Comput Sci 2(4):1–9

    Article  Google Scholar 

  134. Bathla G, Singh P, Singh RK, Cambria E, Tiwari R (2022) Intelligent fake reviews detection based on aspect extraction and analysis using deep learning. Neural Comput Appl 34:20213–20229

    Article  Google Scholar 

  135. Mabokela KR, Celik T, Raborife M (2023) Multilingual sentiment analysis for under-resourced languages: a systematic review of the landscape. IEEE Access 11(October 2022):15996–16020

    Article  Google Scholar 

  136. Zou Y, Zhu B, Hu X, Gui T, Zhang Q (2021) Low-resource dialogue summarization with domain-agnostic multi-source pretraining. In: EMNLP 2021 - 2021 conf. empir. methods nat. lang. process. proc., pp 80–91

  137. Liu C (2015) IncreSTS: towards real-time incremental short text summarization on comment streams from social network services. IEEE Trans Knowl Data Eng 27(11):2986–3000

    Article  Google Scholar 

  138. Guerra PHC, Veloso A, Meira W, Almeida V (2011) From bias to opinion: A transfer-learning approach to real-time sentiment analysis. In: Proc. ACM SIGKDD int. conf. knowl. discov. data min., no. August, pp 150–158

  139. Hu M, Liu B (2006) Opinion extraction and summarization on the web. Proc Natl Conf Artif Intell 2:1621–1624

    Google Scholar 

  140. Kangale A, Kumar S, Naeem MA, Williams M, Tiwari M (2015) Mining consumer reviews to generate ratings of different product attributes while producing feature- based review-summary. Int J Syst Sci 47(February 2016):3272–3286

    Google Scholar 

  141. Hariharan S, Srimathi R, Sivasubramanian M (2010) Opinion mining and summarization of reviews in web forums. In: COMPUTE 2010—the 3rd annual ACM Bangalore conference

  142. Kim Amplayo R, Brazinskas A, Suhara Y, Wang X, Liu B (2022) Beyond opinion mining: summarizing opinions of customer reviews, vol 1, no 1. Association for Computing Machinery

  143. Ardilla ZN, Sari TI, Hayatin N, Fatichah C Sarcasm detection on news headline bidirectional-LSTM with glove embeddings using multilayer, pp 2–7

  144. Liu M, Shang Y, Yue Q, Zhou J (2021) Detecting fake reviews using multidimensional representations with fine-grained aspects plan. IEEE Access 9:3765–3773

    Article  Google Scholar 

  145. Dragoni M, Federici M, Rexha A (2019) An unsupervised aspect extraction strategy for monitoring real-time reviews stream. Inf Process Manag 56(3):1103–1118

    Article  Google Scholar 

  146. AbdulAziz A, Starkey A (2020) Predicting supervise machine learning performances for sentiment analysis using contextual-based approaches. IEEE Access 8:17722–17733

    Article  Google Scholar 

  147. Banerjee A, Bhattacharjee M, Ghosh K, Chatterjee S (2020) Synthetic minority oversampling in addressing imbalanced sarcasm detection in social media. Multimed Tools Appl 79(47–48):35995–36031

    Article  Google Scholar 

  148. Xu H, Liu H, Jiao P, Wang W (2021) Transformer reasoning network for personalized review summarization. SIGIR 2021:1452–1461

    Google Scholar 

  149. Zuo E, Zhao H, Chen B, Chen Q (2020) Context-specific heterogeneous graph convolutional network for implicit sentiment analysis. IEEE Access 8:37967–37975

    Article  Google Scholar 

  150. Elsahar H, Coavoux M, Gallé M, Rozen J (2021) Self-supervised and controlled multi-document opinion summarization. In: EACL 2021—16th conf. eur. chapter assoc. comput. linguist. proc. conf., pp 1646–1662

  151. Akhtar MS, Sawant P, Sen S, Ekbal A, Bhattacharyya P (2018) Improving word embedding coverage in less-resourced languages through multi-linguality and cross-linguality: a case study with aspect-based sentiment analysis. In: ACM trans. Asian low-resour. lang. inf. process., vol 18, no 2

  152. Chaturvedi I, Ragusa E, Gastaldo P, Zunino R, Cambria E (2018) Bayesian network based extreme learning machine for subjectivity detection. J Frankl Inst 355(4):1780–1797

    Article  MathSciNet  Google Scholar 

  153. Eke CI, Norman AA, Shuib L (2021) Context-based feature technique for sarcasm identification in benchmark datasets using deep learning and BERT model. IEEE Access 9:48501–48518

    Article  Google Scholar 

  154. Akhtar N, Zubair N, Kumar A, Ahmad T (2017) Aspect based Sentiment oriented summarization of hotel reviews. Procedia Comput Sci 115:563–571

    Article  Google Scholar 

  155. Tran TA, Duangsuwan J, Wettayaprasit W (2021) Automatic aspect-based sentiment summarization for visual, structured, and textual summaries. ECTI Trans Comput Inf Technol 15(1):50–72

    Google Scholar 

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Acknowledgements

This work is supported by: Kementerian Pengajian Tinggi Malaysia, Fundamental Research Grant Scheme (FRGS) by code number FRGS/1/2020/ICT02/UMS/02/2; Language Engineering and Application Development (LEAD) research group of Faculty of Computing and Informatics, Universiti Malaysia Sabah; and Lembaga Pengembangan Publikasi Ilmiah (LPPI) University of Muhammadiyah Malang (UMM), Indonesia.

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Authors and Affiliations

  1. Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

    Nur Hayatin, Suraya Alias & Lai Po Hung

  2. Informatics Department of Engineering Faculty, University of Muhammadiyah Malang, Malang, Indonesia

    Nur Hayatin

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  1. Nur Hayatin
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  2. Suraya Alias
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  3. Lai Po Hung
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Nur Hayatin conducted the experiment and composed the manuscript with the assistance of Suraya Alias, who provided project supervision and collaborated with Lai Po Hung in manuscript review.

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Correspondence to Suraya Alias.

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Hayatin, N., Alias, S. & Hung, L.P. Trends and challenges in sentiment summarization: a systematic review of aspect extraction techniques. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02075-w

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