Skip to main content
SpringerLink
Log in

Predicting emotions in online social networks: challenges and opportunities

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Online social networking has become a popular means of information exchange and social interactions. Users of these platforms generate massive amounts of data about their relationships, behaviors, interests, opinions, locations visited, items purchased, and subjective experiences of various aspects of life. Moreover, these platforms enable people from wide-ranging social and cultural backgrounds to synergize and interact. One interesting area of research is the emotional dimensions contained in this user-generated content, specifically, emotion detection and prediction, which involve the extraction and analysis of emotions in social network data. This study aimed to provide a comprehensive overview and better understanding of the current state of research regarding emotion detection in online social networks by performing a systematic literature review (SLR). SLRs help identify the gaps, challenges, and opportunities in a field of study through a careful examination of current research to understand the methods and results, ultimately highlighting methodological concerns that can be used to improve future work in the field. Hence, we collected and analyzed studies that focused on emotion in social network posts and discussed various topics published in digital databases between 2010 and December 2020. Over 239 articles were initially included in the collection, and after the selection process and application of our quality criteria, 104 articles were examined, and the results showed a robust extant body of literature on the text-based emotion analysis model, while the image-based requires more attention as well as the multiple modality emotion analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Abdullah M, Hadzikadicy M, Shaikhz S (2018) SEDAT: sentiment and emotion detection in Arabic text using CNN-LSTM deep learning. In: 2018 17th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 835–840

  2. Abdul-Mageed M, Ungar L (2017) Emonet: fine-grained emotion detection with gated recurrent neural networks. In: proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: long papers). Pp 718–728

  3. Akaichi J (2013) Social networks’ Facebook’statutes updates mining for sentiment classification. In: 2013 international conference on social computing. IEEE:886–891

  4. Akuma S, Iqbal R, Jayne C, Doctor F (2016) Comparative analysis of relevance feedback methods based on two user studies. Comput Human Behav 60:138–146

    Article  Google Scholar 

  5. Alhamid MF, Alsahli S, Rawashdeh M, Alrashoud M (2017) Detection and visualization of Arabic emotions on social emotion map. In: 2017 IEEE international symposium on multimedia (ISM). IEEE, pp 378–381

  6. Alm CO, Roth D, Sproat R (2005) Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of human language technology conference and conference on empirical methods in natural language processing. pp. 579–586

  7. Almehmadi A, Bourque M, El-Khatib K (2013) A tweet of the mind: automated emotion detection for social media using brain wave pattern analysis. In: 2013 international conference on social computing. IEEE, pp 987–991

  8. Anagnostopoulos C-N, Iliou T, Giannoukos I (2015) Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011. Artif Intell Rev 43:155–177

    Article  Google Scholar 

  9. Anjaria M, Guddeti RMR (2014) Influence factor based opinion mining of twitter data using supervised learning. In: 2014 sixth international conference on communication systems and networks (COMSNETS). IEEE, pp 1–8

  10. Ashkezari-Toussi S, Kamel M, Sadoghi-Yazdi H (2019) Emotional maps based on social networks data to analyze cities emotional structure and measure their emotional similarity. Cities 86:113–124

    Article  Google Scholar 

  11. Baali M, Ghneim N (2019) Emotion analysis of Arabic tweets using deep learning approach. J Big Data 6:89

    Article  Google Scholar 

  12. Bahrainian S-A, Dengel A (2013) Sentiment analysis using sentiment features. In: 2013 IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT). IEEE:26–29

  13. Balahur A, Hermida JM, Montoyo A (2011) Detecting emotions in social affective situations using the emotinet knowledge base. In: International Symposium on Neural Networks. Springer, pp. 611–620

  14. Banks D, Carley K (1994) Metric inference for social networks. J Classif 11:121–149

    Article  MATH  Google Scholar 

  15. Barrett LF, Adolphs R, Marsella S, Martinez AM, Pollak SD (2019) Emotional expressions reconsidered: challenges to inferring emotion from human facial movements. Psychol Sci public Interes 20:1–68

    Article  Google Scholar 

  16. Baumeister RF, Leary MR (1997) Writing narrative literature reviews. Rev Gen Psychol 1:311–320

    Article  Google Scholar 

  17. Bengio Y (2009) Learning deep architectures for AI. Now Publishers Inc

  18. Bernabé-Moreno J, Tejeda-Lorente A, Porcel C, Fujita H, Herrera-Viedma E (2015) Emotional profiling of locations based on social media. Procedia Comput Sci 55:960–969

    Article  Google Scholar 

  19. Borth D, Ji R, Chen T, et al (2013) Large-scale visual sentiment ontology and detectors using adjective noun pairs. In: proceedings of the 21st ACM international conference on multimedia. Pp 223–232

  20. Bravo-Marquez F, Frank E, Mohammad SM, Pfahringer B (2016) Determining word-emotion associations from tweets by multi-label classification. In: 2016 IEEE/WIC/ACM international conference on web intelligence (WI). IEEE, pp 536–539

  21. Bravo-Marquez F, Frank E, Pfahringer B, Mohammad SM (2019) AffectiveTweets: a Weka package for analyzing affect in tweets

  22. Brereton P, Kitchenham BA, Budgen D, Turner M, Khalil M (2007) Lessons from applying the systematic literature review process within the software engineering domain. J Syst Softw 80:571–583

    Article  Google Scholar 

  23. Brest P, Krieger LH (2010) Problem solving, decision making, and professional judgment: a guide for lawyers and policymakers. Oxford University Press

  24. Broad CD (1954) Emotion and sentiment. J Aesthet Art Crit 13:203–214

    Article  Google Scholar 

  25. Buechel S, Hahn U (2017) Emobank: studying the impact of annotation perspective and representation format on dimensional emotion analysis. In: proceedings of the 15th conference of the European chapter of the Association for Computational Linguistics: volume 2, short papers. Pp 578–585

  26. Butts CT (2008) Social network analysis: a methodological introduction. Asian J Soc Psychol 11:13–41

    Article  Google Scholar 

  27. Cai W, Jia J, Han W (2018) Inferring emotions from image social networks using group-based factor graph model. In: 2018 IEEE international conference on multimedia and expo (ICME). IEEE, pp 1–6

  28. Cambria E, Schuller B, Xia Y, Havasi C (2013) New avenues in opinion mining and sentiment analysis. IEEE Intell Syst 28:15–21

    Article  Google Scholar 

  29. Cambria E, Das D, Bandyopadhyay S, Feraco A (2017) Affective computing and sentiment analysis. In: A practical guide to sentiment analysis. Springer, pp. 1–10

  30. Chen H, Zimbra D (2010) AI and opinion mining. IEEE Intell Syst 25:74–80

    Google Scholar 

  31. Chen Y-L, Chang C-L, Yeh C-S (2017) Emotion classification of YouTube videos. Decis Support Syst 101:40–50

    Article  Google Scholar 

  32. Chopade CR (2015) Text based emotion recognition: a survey. Int J Sci Res 4:409–414

    Google Scholar 

  33. Clos J, Bandhakavi A, Wiratunga N, Cabanac G (2017) Predicting emotional reaction in social networks. In: European Conference on Information Retrieval. Springer, pp. 527–533

  34. Corchs S, Fersini E, Gasparini F (2019) Ensemble learning on visual and textual data for social image emotion classification. Int J Mach Learn Cybern 10:2057–2070

    Article  Google Scholar 

  35. Counts MDCS, Gamon M (2012) Not all moods are created equal! Exploring human emotional states in social media. In: proc. Int. AAAI Conf. Web social media (ICWSM). Pp 1–8

  36. Coviello L, Sohn Y, Kramer ADI, Marlow C, Franceschetti M, Christakis NA, Fowler JH (2014) Detecting emotional contagion in massive social networks. PLoS One 9:e90315

    Article  Google Scholar 

  37. Dai W, Han D, Dai Y, Xu D (2015) Emotion recognition and affective computing on vocal social media. Inf Manag 52:777–788

    Article  Google Scholar 

  38. Daugherty PR, Wilson HJ (2018) Human+ machine: reimagining work in the age of AI. Harvard Business Press

  39. De Choudhury M, Gamon M, Counts S, Horvitz E (2013) Predicting depression via social media. In: Proceedings of the International AAAI Conference on Web and Social Media

  40. Degenne A, Forsé M (1999) Introducing social networks. Sage

  41. Demszky D, Movshovitz-Attias D, Ko J, et al (2020) Goemotions: a dataset of fine-grained emotions. arXiv Prepr arXiv200500547

  42. Deng J, Cummins N, Han J, et al (2016) The university of Passau open emotion recognition system for the multimodal emotion challenge. In: Chinese Conference on Pattern Recognition. Springer, pp. 652–666

  43. Deshpande M, Rao V (2017) Depression detection using emotion artificial intelligence. In: 2017 international conference on intelligent sustainable systems (ICISS). IEEE, pp 858–862

  44. Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv Prepr arXiv181004805

  45. Diaz-Aviles E, Orellana-Rodriguez C, Nejdl W (2012) Taking the pulse of political emotions in Latin America based on social web streams. In: 2012 Eighth Latin American web congress. IEEE 40–47

  46. Egger M, Ley M, Hanke S (2019) Emotion recognition from physiological signal analysis: a review. Electron Notes Theor Comput Sci 343:35–55

    Article  Google Scholar 

  47. Ekman P (1992) An argument for basic emotions. Cogn Emot 6:169–200

    Article  Google Scholar 

  48. Estrada MLB, Cabada RZ, Bustillos RO, Graff M (2020) Opinion mining and emotion recognition applied to learning environments. Expert Syst Appl 150:113265

    Article  Google Scholar 

  49. Felbo B, Mislove A, Søgaard A, et al (2017) Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. arXiv Prepr arXiv170800524

  50. Gaind B, Syal V, Padgalwar S (2019) Emotion detection and analysis on social media. arXiv Prepr arXiv190108458

  51. Gajarla V, Gupta A (2015) Emotion detection and sentiment analysis of images. Georg Inst Technol

  52. Garcia-Crespo A, Colomo-Palacios R, Gomez-Berbis JM, Ruiz-Mezcua B (2010) SEMO: a framework for customer social networks analysis based on semantics. J Inf Technol 25:178–188

    Article  Google Scholar 

  53. Garcia-Garcia JM, Penichet VMR, Lozano MD (2017) Emotion detection: a technology review. In: Proceedings of the XVIII international conference on human computer interaction. pp. 1–8

  54. Garton L, Haythornthwaite C, Wellman B (1997) Studying online social networks. J Comput Commun 3:JCMC313

  55. Geetha S, Kumar KV (2019) Tweet analysis based on distinct opinion of social media users’. In: Advances in Big Data and Cloud Computing. Springer, pp. 251–261

  56. Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224N Proj report, Stanford 1:2009

  57. Grunspan DZ, Wiggins BL, Goodreau SM (2014) Understanding classrooms through social network analysis: a primer for social network analysis in education research. CBE—Life Sci Educ 13:167–178

    Article  Google Scholar 

  58. Gupta N, Gilbert M, Di Fabbrizio G (2013) Emotion detection in email customer care. Comput Intell 29:489–505

    Article  MathSciNet  Google Scholar 

  59. Hasan M, Rundensteiner E, Agu E (2014) Emotex: detecting emotions in twitter messages

  60. Hasan M, Agu E, Rundensteiner E (2014) Using hashtags as labels for supervised learning of emotions in twitter messages. In: ACM SIGKDD workshop on health informatics, New York, USA

  61. Hasan M, Rundensteiner E, Agu E (2019) Automatic emotion detection in text streams by analyzing twitter data. Int J Data Sci Anal 7:35–51

    Article  Google Scholar 

  62. Hirat R, Mittal N (2015) A survey on emotion detection techniques using text in blogposts. Int Bull Math Res 2:180–187

    Google Scholar 

  63. Huang J, Xiang C, Yuan S, et al (2019) Character-aware convolutional recurrent networks with self-attention for emotion detection on twitter. In: 2019 international joint conference on neural networks (IJCNN). IEEE, pp 1–8

  64. Hussien WA, Tashtoush YM, Al-Ayyoub M, Al-Kabi MN (2016) Are emoticons good enough to train emotion classifiers of arabic tweets? In: 2016 7th international conference on computer science and information technology (CSIT). IEEE, pp 1–6

  65. Illendula A, Sheth A (2019) Multimodal emotion classification. In: companion proceedings of the 2019 world wide web conference. Pp 439–449

  66. Jiang Y-G, Xu B, Xue X (2014) Predicting emotions in user-generated videos. In: Proceedings of the AAAI Conference on Artificial Intelligence

  67. Jindal S, Singh S (2015) Image sentiment analysis using deep convolutional neural networks with domain specific fine tuning. In: 2015 international conference on information processing (ICIP). IEEE, pp 447–451

  68. Kao EC-C, Liu C-C, Yang T-H, et al (2009) Towards text-based emotion detection a survey and possible improvements. In: 2009 international conference on information management and engineering. IEEE, pp 70–74

  69. Karamibekr M, Ghorbani AA (2013) A structure for opinion in social domains. In: 2013 international conference on social computing. IEEE:264–271

  70. Karyotis C, Doctor F, Iqbal R, James A, Chang V (2018) A fuzzy computational model of emotion for cloud based sentiment analysis. Inf Sci (Ny) 433:448–463

    Article  Google Scholar 

  71. Kitchenham B (2004) Procedures for performing systematic reviews. Keele, UK, Keele Univ 33:1–26

    Google Scholar 

  72. Kramer ADI (2010) An unobtrusive behavioral model of" gross national happiness". Proceedings of the SIGCHI conference on human factors in computing systems, In, pp 287–290

    Google Scholar 

  73. Leavitt HJ (1951) Some effects of certain communication patterns on group performance. J Abnorm Soc Psychol 46:38–50

    Article  Google Scholar 

  74. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  75. Li L, Weinberg CR, Darden TA, Pedersen LG (2001) Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17:1131–1142

    Article  Google Scholar 

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

    Article  Google Scholar 

  77. Loia V, Senatore S (2014) A fuzzy-oriented sentic analysis to capture the human emotion in web-based content. Knowledge-Based Syst 58:75–85

    Article  Google Scholar 

  78. Lövheim H (2012) A new three-dimensional model for emotions and monoamine neurotransmitters. Med Hypotheses 78:341–348

    Article  Google Scholar 

  79. Lu J, Batra D, Parikh D, Lee S (2019) Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. arXiv Prepr arXiv190802265

  80. Luyckx K, Vaassen F, Peersman C, Daelemans W (2012) Fine-grained emotion detection in suicide notes: a thresholding approach to multi-label classification. Biomed Inform Insights 5:BII-S8966

    Article  Google Scholar 

  81. Malighetti C, Sciara S, Chirico A, Riva G (2020) Emotional expression of# body on Instagram. Soc Media+ Soc 6:2056305120924771

    Google Scholar 

  82. Manoharan S (2020) Geospatial and social media analytics for emotion analysis of theme park visitors using text mining and gis. J Inf Technol 2:100–107

    Google Scholar 

  83. Marechal C, Mikolajewski D, Tyburek K, et al (2019) Survey on AI-based multimodal methods for emotion detection.

  84. Mashal SX, Asnani K (2017) Emotion intensity detection for social media data. In: 2017 international conference on computing methodologies and communication (ICCMC). IEEE, pp 155–158

  85. McStay A (2018) Emotional AI: the rise of empathic media. Sage

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

    Article  Google Scholar 

  87. Meo R, Sulis E (2017) Processing affect in social media: a comparison of methods to distinguish emotions in tweets. ACM Trans Internet Technol 17:1–25

    Google Scholar 

  88. Mikels JA, Fredrickson BL, Larkin GR, Lindberg CM, Maglio SJ, Reuter-Lorenz PA (2005) Emotional category data on images from the international affective picture system. Behav Res Methods 37:626–630

    Article  Google Scholar 

  89. Moers T, Krebs F, Spanakis G (2018) SEMTec: social emotion mining techniques for analysis and prediction of facebook post reactions. In: International Conference on Agents and Artificial Intelligence. Springer, pp. 361–382

  90. Mohammad S (2012) # Emotional tweets. In: * SEM 2012: The First Joint Conference on Lexical and Computational Semantics–Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012). pp 246–255

  91. Mohammad S, Bravo-Marquez F, Salameh M, Kiritchenko S (2018) Semeval-2018 task 1: affect in tweets. In: proceedings of the 12th international workshop on semantic evaluation. Pp 1–17

  92. Mohammad SM, Bravo-Marquez F (2017) Emotion intensities in tweets. Conscious Emot Exp emerges as a Funct multilevel, Apprais response synchronization

  93. Mohammad SM, Turney PD (2013) Crowdsourcing a word–emotion association lexicon. Comput Intell 29:436–465

    Article  MathSciNet  Google Scholar 

  94. Mohammad SM, Zhu X, Kiritchenko S, Martin J (2015) Sentiment, emotion, purpose, and style in electoral tweets. Inf Process Manag 51:480–499

    Article  Google Scholar 

  95. Nagarsekar U, Mhapsekar A, Kulkarni P, Kalbande DR (2013) Emotion detection from “the SMS of the internet.” In: 2013 IEEE recent advances in intelligent computational systems (RAICS). IEEE, pp 316–321

  96. Naik D, Gorojanam NB, Ramesh D (2020) Community based emotional behaviour using Ekman’s emotional scale. In: International Conference on Innovations for Community Services. Springer, pp. 63–82

  97. Ortony A, Clore GL, Collins A (1988) The cognitive structure of emotions. Cambridge Univ

  98. Otte E, Rousseau R (2002) Social network analysis: a powerful strategy, also for the information sciences. J Inf Sci 28:441–453

    Article  Google Scholar 

  99. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A (2016) Rayyan—a web and mobile app for systematic reviews. Syst Rev 5:1–10

    Article  Google Scholar 

  100. Peng K-C, Chen T, Sadovnik A, Gallagher AC (2015) A mixed bag of emotions: model, predict, and transfer emotion distributions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 860–868

  101. Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Pp 1532–1543

  102. Peters ME, Neumann M, Iyyer M, et al (2018) Deep contextualized word representations. arXiv Prepr arXiv180205365

  103. Petrović S, Osborne M, Lavrenko V (2010) The Edinburgh twitter corpus. In: proceedings of the NAACL HLT 2010 workshop on computational linguistics in a world of social media. Pp 25–26

  104. Plaza-del-Arco FM, Martín-Valdivia MT, Ureña-López LA, Mitkov R (2020) Improved emotion recognition in Spanish social media through incorporation of lexical knowledge. Futur Gener Comput Syst 110:1000–1008

    Article  Google Scholar 

  105. Plutchik R (1980) Emotion. A psychoevolutionary Synth

  106. Purver M, Battersby S (2012) Experimenting with distant supervision for emotion classification. In: proceedings of the 13th conference of the European chapter of the Association for Computational Linguistics. Pp 482–491

  107. Raad BT, Philipp B, Patrick H, Christoph M (2018) Aseds: towards automatic social emotion detection system using facebook reactions. In: 2018 IEEE 20th international conference on high performance computing and communications; IEEE 16th international conference on Smart City; IEEE 4th international conference on data science and systems (HPCC/SmartCity/DSS). IEEE, pp 860–866

  108. Radford A, Jozefowicz R, Sutskever I (2017) Learning to generate reviews and discovering sentiment. arXiv Prepr arXiv170401444

  109. Rambocas M, Gama J (2013) Marketing research: the role of sentiment analysis. Universidade do Porto, Faculdade de Economia do Porto

  110. Rangel F, Rosso P (2016) On the impact of emotions on author profiling. Inf Process Manag 52:73–92

    Article  Google Scholar 

  111. Rao Y, Li Q, Wenyin L, Wu Q, Quan X (2014) Affective topic model for social emotion detection. Neural Netw 58:29–37

    Article  Google Scholar 

  112. Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39:1161–1178

    Article  Google Scholar 

  113. Sailunaz K, Alhajj R (2019) Emotion and sentiment analysis from twitter text. J Comput Sci 36:101003

    Article  Google Scholar 

  114. Sailunaz K, Dhaliwal M, Rokne J, Alhajj R (2018) Emotion detection from text and speech: a survey. Soc Netw Anal Min 8:1–26

    Article  Google Scholar 

  115. Saini S, Rao R, Vaichole V et al (2018) Emotion recognition using multimodal approach. In: 2018 fourth international conference on computing communication control and automation (ICCUBEA). IEEE:1–4

  116. Scherer KR (2005) What are emotions? And how can they be measured? Soc Sci Inf 44:695–729

    Article  Google Scholar 

  117. Serrat O (2017) Social network analysis. In: Knowledge solutions. Springer, pp. 39–43,

  118. Seyeditabari A, Tabari N, Zadrozny W (2018) Emotion detection in text: a review. arXiv Prepr arXiv180600674

  119. Shahheidari S, Dong H, Daud MNR, Bin (2013) Twitter sentiment mining: A multi domain analysis. In: 2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems. IEEE:144–149

  120. Shaver P, Schwartz J, Kirson D, O’connor C (1987) Emotion knowledge: further exploration of a prototype approach. J Pers Soc Psychol 52:1061–1086

    Article  Google Scholar 

  121. Singh VK, Piryani R, Uddin A, Waila P (2013) Sentiment analysis of movie reviews: a new feature-based heuristic for aspect-level sentiment classification. In: 2013 international Mutli-conference on automation, computing, communication, control and compressed sensing (iMac4s). IEEE:712–717

  122. Sintsova V, Musat C, Pu P (2014) Semi-supervised method for multi-category emotion recognition in tweets. In: 2014 IEEE international conference on data mining workshop. IEEE, pp 393–402

  123. Spielberger C (2004) Encyclopedia of applied psychology. Academic press

  124. Stojanovski D, Strezoski G, Madjarov G, Dimitrovski I, Chorbev I (2018) Deep neural network architecture for sentiment analysis and emotion identification of twitter messages. Multimed Tools Appl 77:32213–32242

    Article  Google Scholar 

  125. Suero Montero C, Suhonen J (2014) Emotion analysis meets learning analytics: online learner profiling beyond numerical data. In: proceedings of the 14th Koli calling international conference on computing education research. Pp 165–169

  126. Suttles J, Ide N (2013) Distant supervision for emotion classification with discrete binary values. In: International Conference on Intelligent Text Processing and Computational Linguistics. Springer, pp. 121–136,

  127. Syed AZ (2015) Applying sentiment and emotion analysis on brand tweets for digital marketing. In: 2015 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT). IEEE, pp 1–6

  128. Takahashi Y, Uchida C, Miyaki K, Sakai M, Shimbo T, Nakayama T (2009) Potential benefits and harms of a peer support social network service on the internet for people with depressive tendencies: qualitative content analysis and social network analysis. J Med Internet Res 11:e29

    Article  Google Scholar 

  129. Tan H, Bansal M (2019) Lxmert: learning cross-modality encoder representations from transformers. arXiv Prepr arXiv190807490

  130. Thanapattheerakul T, Mao K, Amoranto J, Chan JH (2018) Emotion in a century: a review of emotion recognition. In: proceedings of the 10th international conference on advances in information technology. Pp 1–8

  131. Tsytsarau M, Palpanas T (2012) Survey on mining subjective data on the web. Data Min Knowl Discov 24:478–514

    Article  MATH  Google Scholar 

  132. Tuveri F, Angioni M (2014) An opinion mining model for generic domains. In: Distributed systems and applications of information filtering and retrieval. Springer, pp. 51–64

  133. Unterkalmsteiner M, Gorschek T, Islam AKMM, Chow Kian Cheng, Permadi RB, Feldt R (2011) Evaluation and measurement of software process improvement—a systematic literature review. IEEE Trans Softw Eng 38:398–424

    Article  Google Scholar 

  134. Valkanas G, Gunopulos D (2013) How the live web feels about events. In: proceedings of the 22nd ACM international conference on Information & Knowledge Management. Pp 639–648

  135. Valkanas G, Gunopulos D (2013) A ui prototype for emotion-based event detection in the live web. In: International workshop on human-computer interaction and knowledge discovery in complex, Unstructured, Big Data. Springer, pp. 89–100

  136. Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. arXiv Prepr arXiv170603762

  137. Vogt T, André E, Wagner J (2008) Automatic recognition of emotions from speech: a review of the literature and recommendations for practical realisation. Affect Emot Human-Comput Interact:75–91

  138. Wan X (2012) A comparative study of cross-lingual sentiment classification. In: 2012 IEEE/WIC/ACM international conferences on web intelligence and intelligent agent technology. IEEE:24–31

  139. Wang W, Chen L, Thirunarayan K, Sheth AP (2012) Harnessing twitter" big data" for automatic emotion identification. In: 2012 international conference on privacy, security, risk and trust and 2012 international Confernece on social computing. IEEE, pp 587–592

  140. Wang X, Jia J, Tang J, Wu B, Cai L, Xie L (2015) Modeling emotion influence in image social networks. IEEE Trans Affect Comput 6:286–297

    Article  Google Scholar 

  141. Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge university press

  142. Wegner DM (1995) A computer network model of human transactive memory. Soc Cogn 13:319–339

    Article  Google Scholar 

  143. Wikarsa L, Thahir SN (2016) A text mining application of emotion classifications of Twitter’s users using Naïve Bayes method international conference on wireless & telematics

  144. Williams G, Mahmoud A (2017) Analyzing, classifying, and interpreting emotions in software users’ tweets. In: 2017 IEEE/ACM 2nd international workshop on emotion awareness in software engineering (SEmotion). IEEE, pp 2–7

  145. Wimmer A, Min B (2006) From empire to nation-state: explaining wars in the modern world, 1816–2001. Am Sociol Rev 71:867–897

    Article  Google Scholar 

  146. Wu B, Jia J, Yang Y, Zhao P, Tang J, Tian Q (2017) Inferring emotional tags from social images with user demographics. IEEE Trans Multimed 19:1670–1684

    Article  Google Scholar 

  147. Xu B, Fu Y, Jiang Y-G, Li B, Sigal L (2016) Heterogeneous knowledge transfer in video emotion recognition, attribution and summarization. IEEE Trans Affect Comput 9:255–270

    Article  Google Scholar 

  148. Xu B, Fu Y, Jiang Y-G, et al (2016) Video emotion recognition with transferred deep feature encodings. In: proceedings of the 2016 ACM on international conference on multimedia retrieval. Pp 15–22

  149. Xu G, Li W, Liu J (2020) A social emotion classification approach using multi-model fusion. Futur Gener Comput Syst 102:347–356

    Article  Google Scholar 

  150. Xu P, Madotto A, Wu C-S, et al (2018) Emo2vec: learning generalized emotion representation by multi-task training. arXiv Prepr arXiv180904505

  151. Xu P, Liu Z, Winata GI, et al (2020) Emograph: capturing emotion correlations using graph networks. arXiv Prepr arXiv200809378

  152. Yadollahi A, Shahraki AG, Zaiane OR (2017) Current state of text sentiment analysis from opinion to emotion mining. ACM Comput Surv 50:1–33

    Article  Google Scholar 

  153. Yang J, Jiang L, Wang C, Xie J (2014) Multi-label emotion classification for tweets in weibo: method and application. In: 2014 IEEE 26th international conference on tools with artificial intelligence. IEEE, pp 424–428

  154. Yassine M, Hajj H (2010) A framework for emotion mining from text in online social networks. In: 2010 IEEE international conference on data mining workshops. IEEE, pp 1136–1142

  155. Ying W, Xiang R, Lu Q (2019) Improving multi-label emotion classification by integrating both general and domain-specific knowledge. In: proceedings of the 5th workshop on Noisy user-generated text (W-NUT 2019). Pp 316–321

  156. You Q, Luo J, Jin H, Yang J (2016) Building a large scale dataset for image emotion recognition: the fine print and the benchmark. arXiv Prepr arXiv160502677

  157. Zhang X, Li W, Ying H et al (2020) Emotion detection in online social networks: a multi-label learning approach. IEEE Internet Things J

  158. Zhang Y, Tang J, Sun J, et al (2010) Moodcast: emotion prediction via dynamic continuous factor graph model. In: 2010 IEEE international conference on data mining. IEEE, pp 1193–1198

  159. Zhao S, Yao H, Gao Y, et al (2016) Predicting personalized emotion perceptions of social images. In: proceedings of the 24th ACM international conference on multimedia. pp 1385–1394

  160. Zhao S, Yao H, Gao Y, Ding G, Chua TS (2016) Predicting personalized image emotion perceptions in social networks. IEEE Trans Affect Comput 9:526–540

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Systems Department, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Saudi Arabia

    Ghadah Alqahtani & Abdulrahman Alothaim

Authors
  1. Ghadah Alqahtani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdulrahman Alothaim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ghadah Alqahtani.

Ethics declarations

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent was not required, as no human or animals were involved.

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alqahtani, G., Alothaim, A. Predicting emotions in online social networks: challenges and opportunities. Multimed Tools Appl 81, 9567–9605 (2022). https://doi.org/10.1007/s11042-022-12345-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12345-w

Keywords

Search

Navigation