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A Structural Topic Modeling-Based Bibliometric Study of Sentiment Analysis Literature

Abstract

Sentiment analysis is an increasingly evolving field of research in computer science. With the considerable number of studies on innovative sentiment analysis available, it is worth the effort to present a review to understand the research on sentiment analysis comprehensively. This study aimed to investigate issues involved in sentiment analysis; for instance, (1) What types of research topics had been covered in sentiment analysis research? (2) How did the research topics evolve with time? (3) What were the topic distributions for major contributors? (4) How did major contributors collaborate in sentiment analysis research? Based on articles retrieved from the Web of Science, this study presented a bibliometric review of sentiment analysis with the basis of a structural topic modeling method to obtain an extensive overview of the research field. We also utilized methods such as regression analysis, geographic visualization, social network analysis, and the Mann–Kendal trend test. Sentiment analysis research had, overall, received a growing interest in academia. In addition, institutions and authors within the same countries/regions were liable to collaborate closely. Highly discussed topics were sentiment lexicons and knowledge bases, aspect-based sentiment analysis, and social network analysis. Several current and potential future directions, such as deep learning for natural language processing, web services, recommender systems and personalization, and education and social issues, were revealed. The findings provided a thorough understanding of the trends and topics regarding sentiment analysis, which could help in efficiently monitoring future research works and projects. Through this study, we proposed a framework for conducting a comprehensive bibliometric analysis.

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Notes

  1. 1.

    Available at: http://home.eduhk.hk/~hxie/data.zip

  2. 2.

    https://ieeexplore.ieee.org/Xplore/home.jsp

  3. 3.

    https://d3js.org/d3.v3.js

  4. 4.

    https://bl.ocks.org/nswamy14/raw/e28ec2c438e9e8bd302f/clusterpurityChart.js

  5. 5.

    https://gephi.org/

  6. 6.

    https://sentic.net/

  7. 7.

    www.nltk.org

References

  1. 1.

    Tirea M. Traders’ behavior effect on stock price evolution. 2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing: IEEE; 2013. p. 273–280.

  2. 2.

    Ma Y, Peng H, Khan T, Cambria E, Hussain A. Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cogn Comput. 2018;10(4):639–50.

    Google Scholar 

  3. 3.

    Agt-Rickauer H, Kutsche R-D, Sack H. Automated recommendation of related model elements for domain models. International conference on model-driven engineering and software development: Springer; 2018. p. 134–58.

  4. 4.

    Ravi K, Ravi V. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl-Based Syst. 2015;89:14–46.

    Google Scholar 

  5. 5.

    Hussein D. A survey on sentiment analysis challenges. J King Saud Univ Eng Sci. 2018;30(4):330–8.

    Google Scholar 

  6. 6.

    Liu B. Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol. 2012;5(1):1–167.

    Google Scholar 

  7. 7.

    Qazi A, Raj RG, Hardaker G, Standing C. A systematic literature review on opinion types and sentiment analysis techniques. Internet Res. 2017;27(3):608–30.

    Google Scholar 

  8. 8.

    Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J. 2014;5(4):1093–113.

    Google Scholar 

  9. 9.

    Han Z, Wu J, Huang C, Huang Q, Zhao M. A review on sentiment discovery and analysis of educational big-data. Wiley Interdisc Rev Data Min Knowl Disc. 2020;10(1):1–22.

    Google Scholar 

  10. 10.

    Poria S, Cambria E, Bajpai R, Hussain A. A review of affective computing: from unimodal analysis to multimodal fusion. Inform Fusion. 2017;37:98–125.

    Google Scholar 

  11. 11.

    Hollenstein N, Rotsztejn J, Troendle M, Pedroni A, Zhang C, Langer N. ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading. Sci Data. 2018;5(1):1–13.

    Google Scholar 

  12. 12.

    Mishra A, Kanojia D, Nagar S, Dey K, Bhattacharyya P. Leveraging cognitive features for sentiment analysis. Proceedings of the 20th SIGNLL conference on computational natural language learning; 2016. p. 156 –166.

  13. 13.

    Liu Q, Wu R, Chen E, Xu G, Su Y, Chen Z, et al. Fuzzy cognitive diagnosis for modelling examinee performance. ACM Trans Intell Syst Technol. 2018;9(4):1–26.

  14. 14.

    Long Y, Xiang R, Lu Q, Huang C-R, Li M. Improving attention model based on cognition grounded data for sentiment analysis. IEEE Trans Affect Comput. 2019:1–14.

  15. 15.

    Long Y, Lu Q, Xiang R, Li M, Huang C R. A cognition based attention model for sentiment analysis. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing; 2017. p. 462–471.

  16. 16.

    Mishra A, Bhattacharyya P. Automatic extraction of cognitive features from gaze data. Cognitively Inspired Natural Language Processing: Springer; 2018. p. 153–69.

  17. 17.

    Xing FZ, Pallucchini F, Cambria E. Cognitive-inspired domain adaptation of sentiment lexicons. Inf Process Manag. 2019;56(3):554–64.

    Google Scholar 

  18. 18.

    Zupic I, Čater T. Bibliometric methods in management and organization. Organ Res Methods. 2015;18(3):429–72.

    Google Scholar 

  19. 19.

    Piryani R, Madhavi D, Singh VK. Analytical mapping of opinion mining and sentiment analysis research during 2000–2015. Inf Process Manag. 2017;53(1):122–50.

    Google Scholar 

  20. 20.

    Keramatfar A, Amirkhani H. Bibliometrics of sentiment analysis literature. J Inf Sci. 2019;45(1):3–15.

    Google Scholar 

  21. 21.

    Mäntylä MV, Graziotin D, Kuutila M. The evolution of sentiment analysis—a review of research topics, venues, and top cited papers. Comput Sci Rev. 2018;27:16–32.

    Google Scholar 

  22. 22.

    Ahlgren O. Research on sentiment analysis: the first decade. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW): IEEE; 2016. p. 890–899.

  23. 23.

    Tubishat M, Idris N, Abushariah MA. Implicit aspect extraction in sentiment analysis: review, taxonomy, opportunities, and open challenges. Inf Process Manag. 2018;54(4):545–63.

    Google Scholar 

  24. 24.

    Zhang D, Wu C, Liu J. Ranking products with online reviews: a novel method based on hesitant fuzzy set and sentiment word framework. J Oper Res Soc. 2020;71(3):528–42.

    Google Scholar 

  25. 25.

    Zhou X, Tao X, Rahman MM, Zhang J. Coupling topic modelling in opinion mining for social media analysis. Proc Int Conf Web Intell. 2017:533–40.

  26. 26.

    Tao X, Zhou X, Zhang J, Yong J. Sentiment analysis for depression detection on social networks. International Conference on Advanced Data Mining and Applications: Springer; 2016. p. 807–810.

  27. 27.

    Liu Z, Liu S, Liu L, Sun J, Peng X, Wang T. Sentiment recognition of online course reviews using multi-swarm optimization-based selected features. Neurocomputing. 2016;185:11–20.

    Google Scholar 

  28. 28.

    Al-Moslmi T, Albared M, Al-Shabi A, Omar N, Abdullah S. Arabic senti-lexicon: constructing publicly available language resources for Arabic sentiment analysis. J Inf Sci. 2018;44(3):345–62.

    Google Scholar 

  29. 29.

    Wu F, Huang Y, Song Y. Structured microblog sentiment classification via social context regularization. Neurocomputing. 2016;175:599–609.

    Google Scholar 

  30. 30.

    Al-Moslmi T, Omar N, Abdullah S, Albared M. Approaches to cross-domain sentiment analysis: a systematic literature review. IEEE Access. 2017;5:16173–92.

    Google Scholar 

  31. 31.

    Kang M, Ahn J, Lee K. Opinion mining using ensemble text hidden Markov models for text classification. Expert Syst Appl. 2018;94:218–27.

    Google Scholar 

  32. 32.

    Calefato F, Lanubile F, Maiorano F, Novielli N. Sentiment polarity detection for software development. Empir Softw Eng. 2018;23(3):1352–82.

    Google Scholar 

  33. 33.

    Li Y, Pan Q, Wang S, Yang T, Cambria E. A generative model for category text generation. Inf Sci. 2018;450:301–15.

    MathSciNet  Google Scholar 

  34. 34.

    Zhang Z, Zou Y, Gan C. Textual sentiment analysis via three different attention convolutional neural networks and cross-modality consistent regression. Neurocomputing. 2018;275:1407–15.

    Google Scholar 

  35. 35.

    García-Pablos A, Cuadros M, Rigau G. W2VLDA: almost unsupervised system for aspect based sentiment analysis. Expert Syst Appl. 2018;91:127–37.

    Google Scholar 

  36. 36.

    Jianqiang Z, Xiaolin G, Xuejun Z. Deep convolution neural networks for twitter sentiment analysis. IEEE Access. 2018;6:23253–60.

    Google Scholar 

  37. 37.

    Hassan A, Mahmood A. Convolutional recurrent deep learning model for sentence classification. IEEE Access. 2018;6:13949–57.

    Google Scholar 

  38. 38.

    Arif MH, Li J, Iqbal M, Liu K. Sentiment analysis and spam detection in short informal text using learning classifier systems. Soft Comput. 2018;22(21):7281–91.

    Google Scholar 

  39. 39.

    Dashtipour K, Gogate M, Li J, Jiang F, Kong B, Hussain A. A hybrid Persian sentiment analysis framework: integrating dependency grammar based rules and deep neural networks. Neurocomputing. 2020;380:1–10.

    Google Scholar 

  40. 40.

    Bahassine S, Madani A, Al-Sarem M, Kissi M. Feature selection using an improved Chi-square for Arabic text classification. J King Saud Univ Comp & Info Sci. 2020;32(2):225–31.

  41. 41.

    Song M, Park H, Shin K. Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean. Inf Process Manag. 2019;56(3):637–53.

  42. 42.

    Dragoni M, Poria S, Cambria E. OntoSenticNet: a commonsense ontology for sentiment analysis. IEEE Intell Syst. 2018;33(3):77–85.

    Google Scholar 

  43. 43.

    Yang Q, Rao Y, Xie H, Wang J, Wang FL, Chan WH, et al. Segment-level joint topic-sentiment model for online review analysis. IEEE Intell Syst. 2019;34(1):43–50.

  44. 44.

    Kumar A, Sebastian TM. Sentiment analysis: a perspective on its past, present and future. Int J Intell Syst Appl. 2012;4(10):1–14.

    Google Scholar 

  45. 45.

    Serrano-Guerrero J, Olivas JA, Romero FP, Herrera-Viedma E. Sentiment analysis: a review and comparative analysis of web services. Inf Sci. 2015;311:18–38.

    Google Scholar 

  46. 46.

    Cambria E, Poria S, Gelbukh A, Thelwall M. Sentiment analysis is a big suitcase. IEEE Intell Syst. 2017;32(6):74–80.

    Google Scholar 

  47. 47.

    Li X, Rao Y, Xie H, Liu X, Wong T-L, Wang FL. Social emotion classification based on noise-aware training. Data Knowl Eng. 2019;123:101605.

    Google Scholar 

  48. 48.

    Liang W, Xie H, Rao Y, Lau RY, Wang FL. Universal affective model for readers’ emotion classification over short texts. Expert Syst Appl. 2018;114:322–33.

    Google Scholar 

  49. 49.

    Li X, Rao Y, Xie H, Lau RYK, Yin J, Wang FL. Bootstrapping social emotion classification with semantically rich hybrid neural networks. IEEE Trans Affect Comput. 2017;8(4):428–42.

    Google Scholar 

  50. 50.

    Rao Y, Xie H, Li J, Jin F, Wang FL, Li Q. Social emotion classification of short text via topic-level maximum entropy model. Inf Manag. 2016;53(8):978–86.

    Google Scholar 

  51. 51.

    Taj S, Shaikh BB, Meghji AF. Sentiment analysis of news articles: a lexicon based approach. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET): IEEE; 2019. p. 1–5.

  52. 52.

    Ilic S, Marrese-Taylor E, Balazs J, Matsuo Y. Deep contextualized word representations for detecting sarcasm and irony. Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis; 2018. p. 2–7.

  53. 53.

    Burgers C, de Lavalette KYR, Steen GJ. Metaphor, hyperbole, and irony: uses in isolation and in combination in written discourse. J Pragmat. 2018;127:71–83.

    Google Scholar 

  54. 54.

    Kim K, Lee J. Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction. Pattern Recogn. 2014;47(2):758–68.

    Google Scholar 

  55. 55.

    Rambocas M, Pacheco BG. Online sentiment analysis in marketing research: a review. J Res Interact Mark. 2018;12(2):146–63.

    Google Scholar 

  56. 56.

    Contratres FG, Alves-Souza SN, Filgueiras LVL, DeSouza LS. Sentiment analysis of social network data for cold-start relief in recommender systems. World Conference on Information Systems and Technologies: Springer; 2018. p. 122–132.

  57. 57.

    Li X, Xie H, Song Y, Zhu S, Li Q, Wang FL. Does summarization help stock prediction? A news impact analysis. IEEE Intell Syst. 2015;30(3):26–34.

    Google Scholar 

  58. 58.

    Li X, Xie H, Wang R, Cai Y, Cao J, Wang F, et al. Empirical analysis: stock market prediction via extreme learning machine. Neural Comput & Applic. 2016;27(1):67–78.

  59. 59.

    Seifollahi S, Shajari M. Word sense disambiguation application in sentiment analysis of news headlines: an applied approach to FOREX market prediction. J Intell Inf Syst. 2019;52(1):57–83.

    Google Scholar 

  60. 60.

    Li X, Xie H, Chen L, Wang J, Deng X. News impact on stock price return via sentiment analysis. Knowl-Based Syst. 2014;69:14–23.

    Google Scholar 

  61. 61.

    Alaei AR, Becken S, Stantic B. Sentiment analysis in tourism: capitalizing on big data. J Travel Res. 2019;58(2):175–91.

    Google Scholar 

  62. 62.

    Kiritchenko S, Zhu X, Mohammad SM. Sentiment analysis of short informal texts. J Artif Intell Res. 2014;50:723–62.

    Google Scholar 

  63. 63.

    Nandal N, Tanwar R, Pruthi J. Machine learning based aspect level sentiment analysis for Amazon products. Spat Inf Res. 2020:1–7.

  64. 64.

    Jiménez-Zafra SM, Taulé M, Martín-Valdivia MT, Ureña-López LA, Martí MA. SFU review SP-NEG: a Spanish corpus annotated with negation for sentiment analysis. A typology of negation patterns. Lang Resour Eval. 2018;52(2):533–69.

    Google Scholar 

  65. 65.

    El Alaoui I, Gahi Y, Messoussi R, Chaabi Y, Todoskoff A, Kobi A. A novel adaptable approach for sentiment analysis on big social data. J Big Data. 2018;5(1):12–30.

    Google Scholar 

  66. 66.

    Dandannavar P, Mangalwede S, Deshpande S. Emoticons and their effects on sentiment analysis of Twitter data. EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing: Springer; 2020. p. 191–201.

  67. 67.

    Peng Q, Zhong M. Detecting spam review through sentiment analysis. JSW. 2014;9(8):2065–72.

    Google Scholar 

  68. 68.

    Guzman E, Maalej W. How do users like this feature? A fine grained sentiment analysis of app reviews. 2014 IEEE 22nd International Requirements Engineering Conference (RE): IEEE; 2014. p. 153–162.

  69. 69.

    Batistič S, van der Laken P. History, evolution and future of big data and analytics: a bibliometric analysis of its relationship to performance in organizations. Br J Manag. 2019;30(2):229–51.

    Google Scholar 

  70. 70.

    Peng B, Guo D, Qiao H, Yang Q, Zhang B, Hayat T, et al. Bibliometric and visualized analysis of China’s coal research 2000-2015. J Clean Prod. 2018;197:1177–89.

  71. 71.

    Song Y, Chen X, Hao T, Liu Z, Lan Z. Exploring two decades of research on classroom dialogue by using bibliometric analysis. Comput Educ. 2019;137:12–31.

    Google Scholar 

  72. 72.

    Martinho VJPD. Best management practices from agricultural economics: mitigating air, soil and water pollution. Sci Total Environ. 2019;688:346–60.

    Google Scholar 

  73. 73.

    Jiang Y, Ritchie BW, Benckendorff P. Bibliometric visualisation: an application in tourism crisis and disaster management research. Curr Issue Tour. 2019;22(16):1925–57.

    Google Scholar 

  74. 74.

    Pang R, Zhang X. Achieving environmental sustainability in manufacture: a 28-year bibliometric cartography of green manufacturing research. J Clean Prod. 2019;233:84–99.

    Google Scholar 

  75. 75.

    Chen X, Wang S, Tang Y, Hao T. A bibliometric analysis of event detection in social media. Online Inf Rev. 2019;43(1):29–52.

    Google Scholar 

  76. 76.

    Chen X, Lun Y, Yan J, Hao T, Weng H. Discovering thematic change and evolution of utilizing social media for healthcare research. BMC Med Inform Decis Making. 2019;19(2):39–53.

    Google Scholar 

  77. 77.

    Chen X, Liu Z, Wei L, Yan J, Hao T, Ding R. A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008-2017. BMC Med Inform Decis Making. 2018;18(5):55–69.

    Google Scholar 

  78. 78.

    Hao T, Chen X, Li G, Yan J. A bibliometric analysis of text mining in medical research. Soft Comput. 2018;22(23):7875–92.

    Google Scholar 

  79. 79.

    Chen X, Xie H, Wang FL, Liu Z, Xu J, Hao T. A bibliometric analysis of natural language processing in medical research. BMC Med Inform Decis Making. 2018;18(1):1–14.

    Google Scholar 

  80. 80.

    Chen X, Zhang X, Xie H, Wang FL, Yan J, Hao T. Trends and features of human brain research using artificial intelligence techniques: a bibliometric approach. International Workshop on Human Brain and Artificial Intelligence: Springer; 2019. p. 69–83.

  81. 81.

    Chen X, Xie H, Cheng G, Poon LK, Leng M, Wang FL. Trends and features of the applications of natural language processing techniques for clinical trials text analysis. Appl Sci. 2020;10(6):2157.

    Google Scholar 

  82. 82.

    Chen X, Zou D, Xie H. Fifty years of British Journal of Educational Technology: a topic modeling based bibliometric perspective. Br J Educ Technol. 2020:1–17.

  83. 83.

    Roberts ME, Stewart BM, Tingley D, Lucas C, Leder-Luis J, Gadarian SK, et al. Structural topic models for open-ended survey responses. Am J Polit Sci. 2014;58(4):1064–82.

  84. 84.

    Bennett R, Vijaygopal R, Kottasz R. Attitudes towards autonomous vehicles among people with physical disabilities. Transp Res A Policy Pract. 2019;127:1–17.

    Google Scholar 

  85. 85.

    Garcia-Rudolph A, Laxe S, Saurí J, Guitart MB. Stroke survivors on Twitter: sentiment and topic analysis from a gender perspective. J Med Internet Res. 2019;21(8):e14077.

    Google Scholar 

  86. 86.

    Hsu A, Brandt J, Widerberg O, Chan S, Weinfurter A. Exploring links between national climate strategies and non-state and subnational climate action in nationally determined contributions (NDCs). Clim Pol. 2020;20(4):443–57.

    Google Scholar 

  87. 87.

    Korfiatis N, Stamolampros P, Kourouthanassis P, Sagiadinos V. Measuring service quality from unstructured data: a topic modeling application on airline passengers’ online reviews. Expert Syst Appl. 2019;116:472–86.

    Google Scholar 

  88. 88.

    Chandelier M, Steuckardt A, Mathevet R, Diwersy S, Gimenez O. Content analysis of newspaper coverage of wolf recolonization in France using structural topic modeling. Biol Conserv. 2018;220:254–61.

    Google Scholar 

  89. 89.

    Chen X, Yu G, Cheng G, Hao T. Research topics, author profiles, and collaboration networks in the top-ranked journal on educational technology over the past 40 years: a bibliometric analysis. J Comput Educ. 2019;6(4):563–85.

    Google Scholar 

  90. 90.

    Chen X, Zou D, Cheng G, Xie H. Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: a retrospective of all volumes of Computers & Education. Comput Educ. 2020;151:1–53.

    Google Scholar 

  91. 91.

    Rothschild JE, Howat AJ, Shafranek RM, Busby EC. Pigeonholing partisans: stereotypes of party supporters and partisan polarization. Polit Behav. 2019;41(2):423–43.

    Google Scholar 

  92. 92.

    Chen X, Chen J, Cheng G, Gong T. Topics and trends in artificial intelligence assisted human brain research. PLoS One. 2020;15(4):e0231192.

    Google Scholar 

  93. 93.

    Roberts ME, Stewart BM, Tingley D. Stm: R package for structural topic models. J Stat Softw. 2014;10(2):1–40.

    Google Scholar 

  94. 94.

    Jiang H, Qiang M, Lin P. A topic modeling based bibliometric exploration of hydropower research. Renew Sust Energ Rev. 2016;57:226–37.

    Google Scholar 

  95. 95.

    Farrell J. Corporate funding and ideological polarization about climate change. Proc Natl Acad Sci USA. 2016;113(1):92–7.

    Google Scholar 

  96. 96.

    Tvinnereim E, Fløttum K. Explaining topic prevalence in answers to open-ended survey questions about climate change. Nat Clim Chang. 2015;5(8):744–7.

    Google Scholar 

  97. 97.

    Jiang H, Qiang M, Fan Q, Zhang M. Scientific research driven by large-scale infrastructure projects: a case study of the Three Gorges Project in China. Technol Forecast Soc Chang. 2018;134:61–71.

    Google Scholar 

  98. 98.

    Mann HB. Nonparametric tests against trend. Econometrica J Econ Soc. 1945;13:245–59.

    MathSciNet  MATH  Google Scholar 

  99. 99.

    Chen X, Hao T. Quantifying and visualizing the research status of social media and health research field. Social Web and Health Research: Springer; 2019. p. 31–51.

  100. 100.

    Hirsch JE, Buela-Casal G. The meaning of the h-index. Int J Clin Health Psychol. 2014;14(2):161–4.

    Google Scholar 

  101. 101.

    Liu B. Sentiment analysis: mining opinions, sentiments, and emotions: Cambridge University Press; 2015. p. 8.

  102. 102.

    Zhao Y, Qin B, Liu T. Exploiting syntactic and semantic kernels for target-polarity word collocation extraction. 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia): IEEE; 2018. p. 1–6.

  103. 103.

    He J, Song T, Peng W, Sheng Q, Song J. Automatic acquisition of matching patterns for pattern-based parsing on specific Chinese text. 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW): IEEE; 2016. p. 17–20.

  104. 104.

    Xiong S, Ji D. Exploiting capacity-constrained k-means clustering for aspect-phrase grouping. International Conference on Knowledge Science, Engineering and Management: Springer; 2015. p. 370–381.

  105. 105.

    Araque O, Zhu G, García-Amado M, Iglesias CA. Mining the opinionated web: classification and detection of aspect contexts for aspect based sentiment analysis. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW): IEEE; 2016. p. 900–907.

  106. 106.

    Chen G, Zhang Q, Chen D. A pair-wise method for aspect-based sentiment analysis. International Conference on Cognitive Computing: Springer; 2018. p. 18–29.

  107. 107.

    Qasem M, Thulasiraman P, Thulasiram RK. Constrained ant brood clustering algorithm with adaptive radius: a case study on aspect based sentiment analysis. 2017 IEEE Symposium Series on Computational Intelligence (SSCI): IEEE; 2017. p. 1–8.

  108. 108.

    Omurca Sİ, Ekinci E. Using adjusted Laplace smoothing to extract implicit aspects from Turkish hotel reviews. 2018 Innovations in Intelligent Systems and Applications (INISTA): IEEE; 2018.p. 1–6.

  109. 109.

    Singh JP, Irani S, Rana NP, Dwivedi YK, Saumya S, Roy PK. Predicting the “helpfulness” of online consumer reviews. J Bus Res. 2017;70:346–55.

    Google Scholar 

  110. 110.

    Liang T-P, Li X, Yang C-T, Wang M. What in consumer reviews affects the sales of mobile apps: a multifacet sentiment analysis approach. Int J Electron Commer. 2015;20(2):236–60.

    Google Scholar 

  111. 111.

    Garg P, Garg H, Ranga V. Sentiment analysis of the Uri terror attack using Twitter. 2017 International Conference on Computing, Communication and Automation (ICCCA): IEEE; 2017. p. 17–20.

  112. 112.

    Han S, Kavuluru R. On assessing the sentiment of general tweets. Canadian Conference on Artificial Intelligence: Springer; 2015. p. 181–195.

  113. 113.

    Raja M, Swamynathan S. Tweet sentiment analyzer: sentiment score estimation method for assessing the value of opinions in tweets. Proceedings of the International Conference on Advances in Information Communication Technology & Computing: ACM; 2016. p. 1–6.

  114. 114.

    Gul S, Mahajan I, Nisa NT, Shah TA, Asifa J, Ahmad S. Tweets speak louder than leaders and masses: an analysis of tweets about the Jammu and Kashmir elections 2014. Online Inf Rev. 2016;40(7):900–12.

    Google Scholar 

  115. 115.

    Singh P, Sawhney RS, Kahlon KS. Predicting the outcome of Spanish general elections 2016 using Twitter as a tool. International Conference on Advanced Informatics for Computing Research: Springer; 2017. p. 73–83.

  116. 116.

    Dinkić N, Džaković N, Joković J, Stoimenov L, Đukić A. Using sentiment analysis of Twitter data for determining popularity of city locations. International Conference on ICT Innovations: Springer; 2016. p. 156–164.

  117. 117.

    Purnamasari PD, Taqiyuddin M, Ratna AAP. Performance comparison of text-based sentiment analysis using recurrent neural network and convolutional neural network. Proceedings of the 3rd International Conference on Communication and Information Processing: ACM; 2017. p. 19–23.

  118. 118.

    Huang Q, Chen R, Zheng X, Dong Z. Deep sentiment representation based on CNN and LSTM. 2017 International Conference on Green Informatics (ICGI): IEEE; 2017. p. 30–33.

  119. 119.

    Kuta M, Morawiec M, Kitowski J. Sentiment analysis with tree-structured gated recurrent units. International Conference on Text, Speech, and Dialogue: Springer; 2017. p. 74–82.

  120. 120.

    Huang M, Xie H, Rao Y, Feng J, Wang FL. Sentiment strength detection with a context-dependent lexicon-based convolutional neural network. Inf Sci. 2020;520:389–99.

    Google Scholar 

  121. 121.

    Sato M, Orihara R, Sei Y, Tahara Y, Ohsuga A. Text classification and transfer learning based on character-level deep convolutional neural networks. International Conference on Agents and Artificial Intelligence: Springer; 2017. p. 62–81.

  122. 122.

    Bodrunova SS, Blekanov IS, Kukarkin M, Zhuravleva N. Negative a/effect: sentiment of French-speaking users and its impact upon affective hashtags on Charlie Hebdo. International Conference on Internet Science: Springer; 2018. p. 226–241.

  123. 123.

    Chen X, Qin Z, Zhang Y, Xu T. Learning to rank features for recommendation over multiple categories. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval:ACM; 2016. p. 305–314.

  124. 124.

    Chen S, Huang Y, Huang W. Big data analytics on aviation social media: the case of china southern airlines on sina weibo. 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService): IEEE; 2016. p. 152–155.

  125. 125.

    Yun Y, Hooshyar D, Jo J, Lim H. Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review. J Inf Sci. 2018;44(3):331–44.

    Google Scholar 

  126. 126.

    Yan Q, Zhou S, Wu S. The influences of tourists’ emotions on the selection of electronic word of mouth platforms. Tour Manag. 2018;66:348–63.

    Google Scholar 

  127. 127.

    Jayaratna MSH, Bouguettaya A, Dong H, Qin K, Erradi A. Subjective evaluation of market-driven cloud services. 2017 IEEE International Conference on Web Services (ICWS): IEEE; 2017. p. 516–523.

  128. 128.

    López MB, Alor-Hernández G, Sánchez-Cervantes JL, del Pilar S-ZM, Paredes-Valverde MA. EduRP: an educational resources platform based on opinion mining and semantic web. J Univ Comput Sci. 2018;24(11):1515–35.

    Google Scholar 

  129. 129.

    Esparza GG, de Luna A, Zezzatti AO, Hernandez A, Ponce J, Álvarez M, et al. A sentiment analysis model to analyze students reviews of teacher performance using support vector machines. International Symposium on Distributed Computing and Artificial Intelligence: Springer; 2017. p. 157–164.

  130. 130.

    Chauhan GS, Agrawal P, Meena YK. Aspect-based sentiment analysis of students’ feedback to improve teaching–learning process. Information and Communication Technology for Intelligent Systems: Springer; 2019. p. 259–66.

  131. 131.

    de Paula Santos F, Lechugo CP, Silveira-Mackenzie IF. “Speak well” or “complain” about your teacher: a contribution of education data mining in the evaluation of teaching practices. 2016 International Symposium on Computers in Education (SIIE): IEEE; 2016. p. 1–4.

  132. 132.

    Maqsood H, Mehmood I, Maqsood M, Yasir M, Afzal S, Aadil F, et al. A local and global event sentiment based efficient stock exchange forecasting using deep learning. Int J Inf Manag. 2020;50:432–51.

  133. 133.

    García-Díaz V, Espada JP, Crespo RG, G-Bustelo BCP, Lovelle JMC. An approach to improve the accuracy of probabilistic classifiers for decision support systems in sentiment analysis. Appl Soft Comput. 2018;67:822–33.

    Google Scholar 

  134. 134.

    Ma R, Wang K, Qiu T, Sangaiah AK, Lin D, Liaqat HB. Feature-based compositing memory networks for aspect-based sentiment classification in social internet of things. Futur Gener Comput Syst. 2019;92:879–88.

    Google Scholar 

  135. 135.

    Jabreel M, Moreno A. A deep learning-based approach for multi-label emotion classification in tweets. Appl Sci. 2019;9(6):1123–39.

    Google Scholar 

  136. 136.

    Poria S, Majumder N, Hazarika D, Cambria E, Gelbukh A, Hussain A. Multimodal sentiment analysis: addressing key issues and setting up the baselines. IEEE Intell Syst. 2018;33(6):17–25.

    Google Scholar 

  137. 137.

    Sun M, Konstantelos I, Strbac G. A deep learning-based feature extraction framework for system security assessment. IEEE Trans Smart Grid. 2018;10(5):5007–20.

    Google Scholar 

  138. 138.

    Valdivia A, Martínez-Cámara E, Chaturvedi I, Luzón MV, Cambria E, Ong YS, et al. What do people think about this monument? Understanding negative reviews via deep learning, clustering and descriptive rules. J Ambient Intell Humaniz Comput. 2020;11(1):39–52.

  139. 139.

    Ma Y, Peng H, Cambria E. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. Thirty-second AAAI Conference on Artificial Intelligence; 2018. p. 5876–5883.

  140. 140.

    Majumder N, Poria S, Hazarika D, Mihalcea R, Gelbukh A, Cambria E. Dialoguernn: an attentive RNN for emotion detection in conversations. Proc AAAI Conf Artif Intell. 2019;33:6818–25.

    Google Scholar 

  141. 141.

    Zhao W, Peng H, Eger S, Cambria E, Yang M. Towards scalable and reliable capsule networks for challenging NLP applications. Proceedings of the 57th annual meeting of the Association for Computational Linguistics; 2019. p. 1549–1559.

  142. 142.

    Peng H, Ma Y, Li Y, Cambria E. Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowl-Based Syst. 2018;148:167–76.

    Google Scholar 

  143. 143.

    Majumder N, Poria S, Gelbukh A, Akhtar MS, Cambria E, Ekbal A. IARM: inter-aspect relation modeling with memory networks in aspect-based sentiment analysis. Proceedings of the 2018 conference on Empirical Methods in Natural Language Processing; 2018. p. 3402–3411.

  144. 144.

    Al-Smadi M, Al-Ayyoub M, Jararweh Y, Qawasmeh O. Enhancing aspect-based sentiment analysis of Arabic hotels’ reviews using morphological, syntactic and semantic features. Inf Process Manag. 2019;56(2):308–19.

    Google Scholar 

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Funding

The research presented in this study has been supported by the Interdisciplinary Research Scheme of Dean’s Research Fund 2018-19 (FLASS/DRF/IDS-3), Departmental Collaborative Research Fund 2019 (MIT/DCRF-R2/18-19), Small Grant for Academic Staff (MIT/SGA04/19-20) of The Education University of Hong Kong, HKIBS Research Seed Fund 2019/20 (190-009), and Research Seed Fund (102367) of Lingnan University, Hong Kong.

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Correspondence to Haoran Xie.

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Chen, X., Xie, H. A Structural Topic Modeling-Based Bibliometric Study of Sentiment Analysis Literature. Cogn Comput 12, 1097–1129 (2020). https://doi.org/10.1007/s12559-020-09745-1

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Keywords

  • Sentiment analysis
  • Bibliometric
  • Structural topic modeling
  • Social network analysis