Abstract
Research on sentic computing has received intensive attention in recent years, as indicated by the increased availability of academic literature. However, despite the growth in literature and researchers’ interests, there are no reviews on this topic. This study comprehensively explores the current research progress and tendencies, particularly the thematic structure of sentic computing, to provide insights into the issues addressed during the past decade and the potential future of sentic computing. We combined bibliometric analysis and structural topic modeling to examine sentic computing literature in various aspects, including the tendency of annual article count, top journals, countries/regions, institutions, and authors, the scientific collaborations between major contributors, as well as the major topics and their tendencies. We obtained interesting and meaningful findings. For example, sentic computing has attracted growing interest in academia. In addition, Cognitive Computation and Nanyang Technological University were found to be the most productive journal and institution in publishing sentic computing studies, respectively. Moreover, important issues such as cyber issues and public opinion, deep neural networks and personality, financial applications and user profiles, and affective and emotional computing have been commonly addressed by authors focusing on sentic computing. Our study provides a thorough overview of sentic computing, reveals major concerns among scholars during the past decade, and offers insights into the future directions of sentic computing research.
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References
Cambria E, Hussain A. Sentic computing: techniques, tools, and applications. Berlin Heidelberg: Springer; 2012. Cham, Switzerland.
Cambria E, Rajagopal D, Olsher D, Das D. Big social data analysis. Big data computing. 2013;13:401–14.
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.
Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst. 2016;31(2):102–7.
Cambria E, Das D, Bandyopadhyay S, Feraco A. A practical guide to sentiment analysis. Springer; 2017. Cham, Switzerland.
Vashishtha S, Susan S. Sentiment cognition from words shortlisted by fuzzy entropy. IEEE Trans Cogn Dev Syst. 2019;12(3):541–50.
Ayutthaya TSN, Pasupa K, Thai sentiment analysis via bidirectional lstm-cnn model with embedding vectors and sentic features. In, 2018. International joint symposium on artificial intelligence and natural language processing (iSAI-NLP). IEEE. 1–6.
Cambria E, Grassi M, Hussain A, Havasi C. Sentic computing for social media marketing. Multimed Tools Appl. 2012;59(2):557–77.
Huang M, Xie H, Rao Y, Feng J, Wang FL. Sentiment strength detection with a context-dependent lexicon-based convolutional neural network. Inf Sci (Ny). 2020;520:389–99.
Cambria E, Speer R, Havasi C, Hussain A. Senticnet: A publicly available semantic resource for opinion mining. In: 2010 AAAI Fall Symposium Series. 2010. p. 14–18.
Mehta Y, Majumder N, Gelbukh A, Cambria E. Recent trends in deep learning based personality detection. Artif Intell Rev. 2020;53:2313–39.
Boudad N, Faizi R, Thami ROH, Chiheb R. Sentiment analysis in Arabic: A review of the literature. Ain Shams Eng J. 2018;9(4):2479–90.
Wang Z, Ho S-B, Cambria E. A review of emotion sensing: categorization models and algorithms. Multimed Tools Appl. 2020;79:35553–82.
Kumar A, Jaiswal A. Systematic literature review of sentiment analysis on Twitter using soft computing techniques. Concurr Comput Pract Exp. 2020;32(1):e5107.
Sukthanker R, Poria S, Cambria E, Thirunavukarasu R. Anaphora and coreference resolution: A review. Inf Fusion. 2020;59:139–62.
Zhang J, Yin Z, Chen P, Nichele S. Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Inf Fusion. 2020;59:103–26.
Ain QT, Ali M, Riaz A, Noureen A, Kamran M, Hayat B, Rehman A. Sentiment analysis using deep learning techniques: A review. Int J Adv Comput Sci Appl. 2017;8(6):424–33.
Azmi AM, Al-Qabbany AO, Hussain A. Computational and natural language processing based studies of hadith literature: A survey. Artif Intell Rev. 2019;52(2):1369–414.
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.
Chen X, Hao J, Chen J, Hua S, Hao T, Hao T, et al. A bibliometric analysis of the research status of the technology enhanced language learning. In Cham: Springer International Publishing; 2018. p. 169–79.
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.
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 Mak. 2018;18(1):1–14.
Keramatfar A, Amirkhani H. Bibliometrics of sentiment analysis literature. J Inf Sci. 2019;45(1):3–15.
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.
Blei DM. Probabilistic topic models. Commun ACM. 2012;55(4):77–84.
Blei DM, Edu BB, Ng AY, Edu AS, Jordan MI, Edu JB. Latent Dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.
Hu N, Zhang T, Gao B, Bose I. What do hotel customers complain about? Text analysis using structural topic model. Tour Manag. 2019;72:417–26.
Lester CA, Wang M, Vydiswaran VGV. Describing the patient experience from Yelp reviews of community pharmacies. J Am Pharm Assoc. 2019;59(3):349–55.
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.
Chen X, Xie H, Cheng G, Poon LKM, Leng M, Wang FL. Trends and deatures of the applications of natural language processing techniques for clinical trials text analysis. Appl Sci. 2020;10(6):2157–93.
Bennett R, Vijaygopal R, Kottasz R. Willingness of people who are blind to accept autonomous vehicles: An empirical investigation. Transp Res Part F Traffic Psychol Behav. 2020;69:13–27.
Cambria E, Hussain A, Havasi C, Eckl C. Sentic computing: Exploitation of common sense for the development of emotion-sensitive systems. In: Development of Multimodal Interfaces: Active Listening and Synchrony. Springer; 2010. p. 148–56.
Svensson G. SSCI and its impact factors: A “prisoner’s dilemma”? Eur J Mark. 2010;44(1/2):23–33.
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.
Gutiérrez-Salcedo M, Martínez MÁ, Moral-Munoz JA, Herrera-Viedma E, Cobo MJ. Some bibliometric procedures for analyzing and evaluating research fields. Appl Intell. 2018;48(5):1275–87.
Roberts ME, Stewart BM, Tingley D. stm: R package for structural topic models. J Stat Softw. 2014;10(2):1–40.
Chen X, Chen J, Cheng G, Gong T. Topics and trends in artificial intelligence assisted human brain research. PLoS One. 2020;15(4):e0231192.
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;51(3):692–708.
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 Computer & Education. Comput Educ. 2020;151:1–21.
Mann HB. Nonparametric tests against trend. Econom J Econom Soc. 1945;13:245–59.
Kendall MG. Rank correlation methods. Oxford: Griffin; 1948.
Chen X, Ding R, Xu K, Wang S, Hao T, Zhou Y. A bibliometric review of natural language processing empowered mobile computing. Wirel Commun Mob Comput. 2018;1–21.
Poria S, Cambria E, Gelbukh A. Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Syst. 2016;108:42–9.
Poria S, Cambria E, Howard N, Huang G-B, Hussain A. Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing. 2016;174:50–9.
Saif H, He Y, Fernandez M, Alani H. Contextual semantics for sentiment analysis of Twitter. Inf Process Manag. 2016;52(1):5–19.
Majumder N, Poria S, Gelbukh A, Cambria E. Deep learning-based document modeling for personality detection from text. IEEE Intell Syst. 2017;32(2):74–9.
Kasun LLC, Zhou H, Huang G-B, Vong CM. Representational learning with extreme learning machine for big data. IEEE Intell Syst. 2013;28(6):31–4.
Poria S, Cambria E, Winterstein G, Huang G-B. Sentic patterns: Dependency-based rules for concept-level sentiment analysis. Knowledge-Based Syst. 2014;69:45–63.
Manek AS, Shenoy PD, Mohan MC, Venugopal KR. Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web. 2017;20(2):135–54.
Ribeiro FN, Araújo M, Gonçalves P, Gonçalves MA, Benevenuto F. Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Sci. 2016;5(1):1–29.
Cambria E, Poria S, Gelbukh A, Thelwall M. Sentiment analysis is a big suitcase. IEEE Intell Syst. 2017;32(6):74–80.
Dinakar K, Jones B, Havasi C, Lieberman H, Picard R. Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Trans Interact Intell Syst. 2012;2(3):1–30.
Hájek P. Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns. Neural Comput Appl. 2018;29(7):343–58.
Wöllmer M, Weninger F, Knaup T, Schuller B, Sun C, Sagae K, et al. Youtube movie reviews: Sentiment analysis in an audio-visual context. IEEE Intell Syst. 2013;28(3):46–53.
Brandes U, Borgatti SP, Freeman LC. Maintaining the duality of closeness and betweenness centrality. Soc Networks. 2016;44:153–9.
Peng H, Cambria E. CSenticNet: A concept-level resource for sentiment analysis in chinese language. In: International Conference on Computational Linguistics and Intelligent Text Processing. Springer; 2017. p. 90–104.
Li W, Zhu L, Shi Y, Guo K, Zheng Y. User reviews: Sentiment analysis using lexicon integrated two-channel CNN-LSTM family models. Appl Soft Comput. 2020;94:1–11.
Kumari K, Singh JP, Dwivedi YK, Rana NP. Towards cyberbullying-free social media in smart cities: A unified multi-modal approach. Soft Comput. 2020;24(15):11059–70.
Liang G, He W, Xu C, Chen L, Zeng J. Rumor identification in microblogging systems based on users’ behavior. IEEE Trans Comput Soc Syst. 2015;2(3):99–108.
Asghar MZ, Habib A, Habib A, Khan A, Ali R, Khattak A. Exploring deep neural networks for rumor detection. J Ambient Intell Humaniz Comput. 2019;1–19.
Akhtar MS, Ekbal A, Narayan S, Singh V. No, that never happened!! Investigating rumors on Twitter. IEEE Intell Syst. 2018;33(5):8–15.
Ahmad H, Arif A, Khattak AM, Habib A, Asghar MZ, Shah B. Applying deep neural networks for predicting dark triad personality trait of online users. In: International Conference on Information Networking (ICOIN). IEEE; 2020. p. 102–105.
Jain G, Sharma M, Agarwal B. Spam detection in social media using convolutional and long short term memory neural network. Ann Math Artif Intell. 2019;85(1):21–44.
Li X, Xie H, Lau RYK, Wong T-L, Wang F-L. Stock prediction via sentimental transfer learning. IEEE Access. 2018;6:73110–8.
Li Q, Chen Y, Wang J, Chen Y, Chen H. Web media and stock markets: A survey and future directions from a big data perspective. IEEE Trans Knowl Data Eng. 2017;30(2):381–99.
Loughran T, McDonald B. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J Finance. 2011;66(1):35–65.
Xie H, Li Q, Mao X, Li X, Cai Y, Rao Y. Community-aware user profile enrichment in folksonomy. Neural Netw. 2014;58:111–21.
Zhou D, Wu X, Zhao W, Lawless S, Liu J. Query expansion with enriched user profiles for personalized search utilizing folksonomy data. IEEE Trans Knowl Data Eng. 2017;29(7):1536–48.
Jain VK, Kumar S, Fernandes SL. Extraction of emotions from multilingual text using intelligent text processing and computational linguistics. J Comput Sci. 2017;21:316–26.
Chatzakou D, Vakali A. Harvesting opinions and emotions from social media textual resources. IEEE Internet Comput. 2015;19:46–50.
Hernández Farías DI. Irony and sarcasm detection in Twitter: The role of affective content. 2017. Doctoral dissertation, Universitat Politècnica de València.
Farías DIH, Patti V, Rosso P. Irony detection in twitter: The role of affective content. ACM Trans Internet Technol. 2016;16(3):1–24.
Qiu J, Liu C, Li Y, Lin Z. Leveraging sentiment analysis at the aspects level to predict ratings of reviews. Inf Sci (Ny). 2018;451:295–309.
Bi JW, Liu Y, Fan ZP, Cambria E. Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. Int J Prod Res. 2019;57(22):7068–88.
Bertola F, Patti V. Ontology-based affective models to organize artworks in the social semantic web. Inf Process Manag. 2016;52(1):139–62.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. 2012. p. 1097–105.
Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP); 2014. p. 1746–1751.
Yadav S, Ekbal A, Saha S, Bhattacharyya P. Medical sentiment analysis using social media: Towards building a patient assisted system. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018); 2018. p. 2790–2797.
Zhang S, Zhao X, Tian Q. Spontaneous speech emotion recognition using multiscale deep convolutional LSTM. IEEE Trans Affect Comput. 2019:1–10.
Mandhula T, Pabboju S, Gugulotu N. Predicting the customer’s opinion on amazon products using selective memory architecture-based convolutional neural network. J Supercomput. 2020;76:5923–47.
Yang X, Molchanov P, Kautz J. Making convolutional networks recurrent for visual sequence learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE; 2018. p. 6469–6478.
Pasupa K, Ayutthaya TSN. Thai sentiment analysis with deep learning techniques: A comparative study based on word embedding, POS-tag, and sentic features. Sustain Cities Soc. 2019;50:1–14.
Luong M-T, Pham H, Manning CD. Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing; 2015. p. 1412–1421.
Sukhbaatar S, Weston J, Fergus R. End-to-end memory networks. In: Advances in neural information processing systems. 2015. p. 2440–2448.
Mubarok MS, Adiwijaya, Aldhi MD. Aspect-based sentiment analysis to review products using Naïve Bayes. In: AIP Conference Proceedings. AIP Publishing LLC; 2017. p. 20060.
Al-Sabahi K, Zhang Z, Long J, Alwesabi K. An enhanced latent semantic analysis approach for arabic document summarization. Arab J Sci Eng. 2018;43(12):8079–94.
Huang J, Xue Y, Hu X, Jin H, Lu X, Liu Z. Sentiment analysis of Chinese online reviews using ensemble learning framework. Cluster Comput. 2019;22(2):3043–58.
Firmanto A, Sarno R. Aspect-based sentiment analysis using grammatical rules, word similarity and SentiCircle. Int J Intell Eng Syst. 2019;12(5):190–201.
Chowdhary KR. Natural language processing. In: Fundamentals of Artificial Intelligence. Springer; 2020. p. 603–49.
Dehdarbehbahani I, Shakery A, Faili H. Semi-supervised word polarity identification in resource-lean languages. Neural Netw. 2014;58:50–9.
Jimenez S, Gonzalez FA, Gelbukh A, Duenas G. Word2set: WordNet-based word representation rivaling neural word embedding for lexical similarity and sentiment analysis. IEEE Comput Intell Mag. 2019;14(2):41–53.
McShane M. Natural language understanding (NLU, not NLP) in cognitive systems. Ai Mag. 2017;38(4):43–56.
Mishra A, Bhattacharyya P. Automatic extraction of cognitive features from gaze data. In: Cognitively Inspired Natural Language Processing. Springer; 2018. p. 153–69.
Pang J, Rao Y, Xie H, Wang X, Wang FL, Wong T-L, et al. Fast supervised topic models for short text emotion detection. IEEE Trans Cybern. 2019:1–14.
Ekinci E, Omurca SI. A new approach for a domain-independent turkish sentiment seed lexicon compilation. Int Arab J Inf Technol. 2019;16(5):843–53.
Kumar A, Sharma A. Ontology driven social big data analytics for fog enabled sentic-social governance. Scalable Comput Pract Exp. 2019;20(2):223–36.
Rintyarna BS, Sarno R, Fatichah C. Evaluating the performance of sentence level features and domain sensitive features of product reviews on supervised sentiment analysis tasks. J Big Data. 2019;6(1):84–103.
Banerjee S, Bhattacharyya S, Bose I. Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business. Decis Support Syst. 2017;96:17–26.
Firdaus SN, Ding C, Sadeghian A. Topic specific emotion detection for retweet prediction. Int J Mach Learn Cybern. 2019;10(8):2071–83.
Solomon RS, Srinivas P, Das A, Gamback B, Chakraborty T. Understanding the psycho-sociological facets of homophily in social network communities. IEEE Comput Intell Mag. 2019;14(2):28–40.
Dridi A, Atzeni M, Recupero DR. FineNews: Fine-grained semantic sentiment analysis on financial microblogs and news. Int J Mach Learn Cybern. 2019;10(8):2199–207.
Atzeni M, Dridi A, Recupero DR. Using frame-based resources for sentiment analysis within the financial domain. Prog Artif Intell. 2018;7(4):273–94.
Nakikj D, Mamykina L. A park or a highway: Overcoming tensions in designing for socio-emotional and informational needs in online health communities. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. 2017. p. 1304–19.
Zhao Y, Zhang J. Consumer health information seeking in social media: A literature review. Heal Inf Libr J. 2017;34(4):268–83.
Satapathy R, Chaturvedi I, Cambria E, Ho SS, Na JC. Subjectivity detection in nuclear energy tweets. Comput y Sist. 2017;21(4):657–64.
Sindhu I, Daudpota SM, Badar K, Bakhtyar M, Baber J, Nurunnabi M. Aspect-based opinion mining on student’s feedback for faculty teaching performance evaluation. IEEE Access. 2019;7:108729–41.
Casales-Garcia V, Museros L, Sanz I, Falomir Z, Gonzalez-Abril L. Extracting feeling from food colour. In: Advances in Tourism, Technology and Smart Systems. Springer; 2020. p. 15–24.
Sorensen V, Lansing J, Thummanapalli N, Cambria E. Mood of the Planet: Challenging Visions of Big Data in the Arts. Cognit Comput. 2020. https://sentic.net/mood-of-the-planet.pdf. Accessed 11 Dec 2020.
Susanto Y, Livingstone A, Ng B, Cambria E. The hourglass model revisited. IEEE Intell Syst. 2020;35(5):96–102.
Rafeek R, Remya R. Detecting contextual word polarity using aspect based sentiment analysis and logistic regression. In: 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM). IEEE; 2017. p. 102–7.
Poria S, Chaturvedi I, Cambria E, Bisio F. Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE; 2016. p. 4465–4473.
Ma Y, Peng H, Khan T, Cambria E, Hussain A. Sentic LSTM: A hybrid network for targeted aspect-based sentiment analysis. Cognit Comput. 2018;10(4):639–50.
Gupta A, Agrawal D, Chauhan H, Dolz J, Pedersoli M. An attention model for group-level emotion recognition. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction. 2018. p. 611–5.
Wei Q, Zhao Y, Xu Q, Li L, He J, Yu L, et al. A new deep-learning framework for group emotion recognition. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction. 2017. p. 587–92.
Wang K, Zeng X, Yang J, Meng D, Zhang K, Peng X, et al. Cascade attention networks for group emotion recognition with face, body and image cues. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction. 2018. p. 640–5.
Cambria E. An introduction to concept-level sentiment analysis. In: Mexican international conference on artificial intelligence. Springer; 2013. p. 478–83.
Balogh V, Berend G, Diochnos DI, Turán G. Understanding the semantic content of sparse word embeddings using a commonsense knowledge base. In: AAAI. 2020. p. 7399–406.
Srivastava R, Bhatia MPS. Challenges with sentiment analysis of on-line micro-texts. Int J Intell Syst Appl. 2017;9(7):31–40.
Kim K. An improved semi-supervised dimensionality reduction using feature weighting: Application to sentiment analysis. Expert Syst Appl. 2018;109:49–65.
Alami N, En-nahnahi N, Ouatik SA, Meknassi M. Using unsupervised deep learning for automatic summarization of Arabic documents. Arab J Sci Eng. 2018;43(12):7803–15.
Canales L, Strapparava C, Boldrini E, Martínez-Barco P. Intensional learning to efficiently build up automatically annotated emotion corpora. IEEE Trans Affect Comput. 2020;11(2):335–47.
Kalarani P, Selva BS. An embellishment of semantic knowledge base using novel crowd sourcing and graph based methods for improving sentiment analysis. J Theor Appl Inf Technol. 2017;95(15):3543–50.
Hassan A, Abbasi A, Zeng D. Twitter sentiment analysis: A bootstrap ensemble framework. In: 2013 International Conference on Social Computing. IEEE; 2013. p. 357–64.
Bisio F, Meda C, Gastaldo P, Zunino R, Cambria E. Concept-level sentiment analysis with SenticNet. In: A Practical Guide to Sentiment Analysis. Springer; 2017. p. 173–88.
Ho D, Hamzah D, Poria S, Singlish CE, SenticNet: a concept-based sentiment resource for Singapore English. In, . IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. 2018;2018:1285–91.
Cambria E, Li Y, Xing FZ, Poria S, Kwok K. Senticnet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020. p. 105–114.
Vilares D, Peng H, Satapathy R, Cambria E, Babelsenticnet: a commonsense reasoning framework for multilingual sentiment analysis. In, . IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. 2018;2018:1292–8.
Simsek A, Karagoz P. Wikipedia enriched advertisement recommendation for microblogs by using sentiment enhanced user profiles. J Intell Inf Syst. 2020;54(2):245–69.
Liu N, Cheng Y. The academic ranking of world universities. High Educ Eur. 2005;30(2):127–36.
Salmi J. The challenge of establishing world class universities. The World Bank; 2009. Washington DC: NACUBO.
Xie H, Chu H-C, Hwang G-J, Wang C-C. Trends and development in technology-enhanced adaptive/personalized learning: a systematic review of journal publications from 2007 to 2017. Comput Educ. 2019;140:1–16.
Li K, Rollins J, Yan E. Web of Science use in published research and review papers 1997–2017: A selective, dynamic, cross-domain, content-based analysis. Scientometrics. 2018;115(1):1–20.
Wei Y-M, Mi Z-F, Huang Z. Climate policy modeling: an online SCI-E and SSCI based literature review. Omega. 2015;57:70–84.
Wang C-C, Chen C-C. Electronic commerce research in latest decade: a literature review. Int J Electron Commer Stud. 2010;1(1):1–14.
Cambria E, Hussain A. Sentic computing: a common-sense-based framework for concept-level sentiment analysis. Springer; 2015. Cham, Switzerland.
Bell D, Koulouri T, Lauria S, Macredie RD, Sutton J. Microblogging as a mechanism for human–robot interaction. Knowledge-Based Syst. 2014;69:64–77.
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This study was funded by One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19–20), the Research Cluster Fund (RG 78/2019-2020R), and the Interdisciplinary Research Scheme of the Dean’s Research Fund 2019–20 (FLASS/DRF/IDS-2) of The Education University of Hong Kong, and the HKIBS Research Seed Fund 2019/20 (190–009), the Direct Grant (101138), the Lam Woo Research Fund (LWI20011) and the Research Seed Fund (102367) of Lingnan University, Hong Kong.
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Chen, X., Xie, H., Cheng, G. et al. A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis. Cogn Comput 14, 24–47 (2022). https://doi.org/10.1007/s12559-021-09861-6
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DOI: https://doi.org/10.1007/s12559-021-09861-6