Skip to main content
Springer Nature Link
Log in

ReUS: a Real-time Unsupervised System For Monitoring Opinion Streams

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

An actual challenge within the sentiment analysis research area is the extraction of polarity values associated with specific aspects (or opinion targets) contained in user-generated content. This task, called aspect-based sentiment analysis, brings new challenges like the disambiguation of words’ role within a text and the inference of correct polarity values based on the domain in which a text occurs. The former requires strategies able to understand how each word is used in a specific context in order to annotate it as aspect or not. The latter need to be addressed with unsupervised solutions in order to make a system efficient for real-time tasks and at the same time flexible in order to adopt it in any domain without requiring the training of sentiment models. Finally, the deployment of such a system into real-world scenarios needs the development of usable solutions for accessing and analyzing data. This paper presents the ReUS platform: a system integrating an unsupervised approach, based on open information extraction strategies, for performing real-time aspect-based sentiment analysis together with facilities supporting decision-makers in the analysis and visualization of collected data. The ReUS platform has been validated from a quantitative and qualitative perspectives. First, the aspect extraction and polarity inference capabilities have been evaluated on three datasets used in likewise editions of SemEval. Second, a user group has been invited to judge the usability of the platform. The developed platform demonstrated to be suitable for being used into real-world scenarios requiring (i) the capability of processing real-time opinion-based documents streams and (ii) the availability of usable facilities for analyzing and visualizing collected data. Examples of possible analysis and visualizations include the presentation of lists ranking aspects by the importance of their polarity values computed within the whole data repository. This kind of analysis enables, for instance, the discovery of product issues.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. The list of stopwords we used can be found at http://lextek.com/manuals/onix/stopwords1.html

  2. http://wordnet.princeton.edu

  3. http://wjh.harvard.edu/inquirer/spreadsheet_guide.htm

  4. http://mpqa.cs.pitt.edu/corpora/mpqa_corpus/

  5. Details about triple’s elements including their meaning and possible values of each element are available within the official documentation http://nlp.stanford.edu/software/dependencies_manual.pdf

  6. http://angularjs.org

  7. http://getbootstrap.com

  8. http://graphdb.ontotext.com

  9. http://d3js.org

  10. All datasets are available on SemEval websites:

    alt.qcri.org/semeval2014/task4/

    alt.qcri.org/semeval2015/task12

    alt.qcri.org/semeval2016/task5

References

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

    Article  Google Scholar 

  2. Hazarika D, Poria S, Zadeh A, Cambria E, Morency Louis-Philippe, Zimmermann R. Conversational memory network for emotion recognition in dyadic dialogue videos. NAACL; 2018. p. 2122–2132.

  3. Chaturvedi I, Cambria E, Welsch R, Herrera F. Distinguishing between facts and opinions for sentiment analysis: survey and challenges. Information Fusion 2018;44:65–77.

    Article  Google Scholar 

  4. Cambria E, Olsher D, Kwok K. Sentic activation: a two-level affective common sense reasoning framework. AAAI. Toronto; 2012. p. 186–192.

  5. Cambria E. An introduction to concept-level sentiment analysis. Advances in soft computing and its applications, volume 8266 of lecture notes in computer science. In: Castro F, Gelbukh A, and González M, editors. Berlin: Springer; 2013. p. 478–483.

  6. Lo SL, Cambria E, Chiong R, Cornforth D. Multilingual sentiment analysis: from formal to informal and scarce resource languages. Artif Intell Rev 2017;48(4):499–527.

    Article  Google Scholar 

  7. Peng H, Ma Y, Li Y, Cambria E. Learning multi-grained aspect target sequence for chinese sentiment analysis. Knowl-Based Syst 2018;148:167–176.

    Article  Google Scholar 

  8. Bandhakavi A, Wiratunga N, Massie S, Deepak P. Lexicon generation for emotion analysis of text. IEEE Intell Syst 2017;32(1):102–108.

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Oneto L, Bisio F, Cambria E, Anguita D. Statistical learning theory and ELM for big social data analysis. IEEE Comput Intell Mag 2016;11(3):45–55.

    Article  Google Scholar 

  11. Hussain A, Cambria E. Semi-supervised learning for big social data analysis. Neurocomputing 2018;275: 1662–1673.

    Article  Google Scholar 

  12. Li Y, Pan Q, Yang T, Wang S, Tang JL, Cambria E. Learning word representations for sentiment analysis. Cogn Comput 2017;9(6):843–851.

    Article  Google Scholar 

  13. Young T, Hazarika D, Poria S, Cambria E. Recent trends in deep learning based natural language processing. IEEE Comput Intell Mag 2018;13(3):55–75.

    Article  Google Scholar 

  14. Li Y, Pan Q, Wang S, Yang T, Cambria E. A generative model for category text generation. Inf Sci 2018;450:301–315.

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Xia Y, Cambria E, Hussain A, Zhao H. Word polarity disambiguation using bayesian model and opinion-level features. Cogn Comput 2015;7(3):369–380.

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Majumder N, Poria S, Gelbukh A, Cambria E. Deep learning-based document modeling for personality detection from text. IEEE Intell Syst 2017;32(2):74–79.

    Article  Google Scholar 

  19. Satapathy R, Guerreiro C, Chaturvedi I, Cambria E. Phonetic-based microtext normalization for twitter sentiment analysis. ICDM; 2017. p. 407–413.

  20. Rajagopal D, Cambria E, Olsher D, Kwok K. A graph-based approach to commonsense concept extraction and semantic similarity detection. WWW; 2013. p. 565–570.

  21. Zhong X, Sun A, Cambria E. Time expression analysis and recognition using syntactic token types and general heuristic rules. ACL; 2017. p. 420–429.

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

  23. Xing F, Cambria E, Welsch R. Natural language based financial forecasting: a survey. Artif Intell Rev 2018;50(1):49–73.

    Article  Google Scholar 

  24. Ebrahimi M, Hossein A, Sheth A. Challenges of sentiment analysis for dynamic events. IEEE Intell Syst 2017;32(5):70–75.

    Article  Google Scholar 

  25. Cambria E, Hussain A, Durrani T, Havasi C, Eckl C, Munro J. Sentic computing for patient centered applications. IEEE ICSP; 2010. p. 1279–1282.

  26. Valdivia A, Luzon V, Herrera F. Sentiment analysis in tripadvisor. IEEE Intell Syst 2017;32(4):72–77.

    Article  Google Scholar 

  27. Mihalcea R, Garimella A. What men say what women hear: finding gender-specific meaning shades. IEEE Intell Syst 2016;31(4):62–67.

    Article  Google Scholar 

  28. Cavallari S, Zheng V, Cai H, Chang K, Cambria E. Learning community embedding with community detection and node embedding on graphs. CIKM; 2017. p. 377–386.

  29. Chi X u, Cambria E, Tan PS. Adaptive two-stage feature selection for sentiment classification. IEEE SMC; 2017. p. 1238–1243.

  30. Zadeh A, Liang PP, Poria S, Vij P, Cambria E, Morency Louis-Philippe. Multi-attention recurrent network for human communication comprehension. AAAI; 2018. p. 5642–5649.

  31. Young T, Cambria E, Chaturvedi I, Zhou H, Biswas S, Huang M. Augmenting end-to-end dialogue systems with commonsense knowledge. AAAI; 2018. p. 4970–4977.

  32. Minqing H u, Liu B. Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, Washington, USA, August 22-25, 2004. In: Kim W, Kohavi R, Gehrke J, and DuMouchel W, editors. ACM; 2004. p. 168–177.

  33. Poria S, Chaturvedi I, Cambria E, Bisio F. Sentic LDA: improving on LDA with semantic similarity for aspect-based sentiment analysis. IJCNN; 2016. p. 4465–4473.

  34. Poria S, Cambria E, Gelbukh A. Aspect extraction for opinion mining with a deep convolutional neural network. Knowl-Based Syst 2016;108:42–49.

    Article  Google Scholar 

  35. Liu B, Zhang L. A survey of opinion mining and sentiment analysis. Mining text data. In: Aggarwal CC and Zhai CX, editors. Springer; 2012. p. 415–463.

  36. Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst 2016;31(2):102–107.

    Article  Google Scholar 

  37. Bo P, Lee L, Vaithyanathan S. Thumbs up? sentiment classification using machine learning techniques. Proceedings of EMNLP. Philadelphia; 2002. p. 79–86. Association for Computational Linguistics.

  38. Pang B, Lee L. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. ACL; 2004. p. 271–278.

  39. Go A, Bhayani R, Huang L. 2009. Twitter sentiment classification using distant supervision. CS224n Project report, Standford University.

  40. Barbosa L, Feng J. Robust sentiment detection on twitter from biased and noisy data. COLING (Posters); 2010. p. 36–44.

  41. Dragoni M. Shellfbk: an information retrieval-based system for multi-domain sentiment analysis. Proceedings of the 9th international workshop on semantic evaluation, SemEval ’2015, pp 502–509, Denver, Colorado; 2015. Association for Computational Linguistics.

  42. Petrucci G, Dragoni M. An information retrieval-based system for multi-domain sentiment analysis. Semantic web evaluation challenges - second semwebeval challenge at ESWC 2015, portorož, Slovenia, May 31 - June 4 (2015), Revised Selected Papers, volume 548 of Communications in Computer and Information Science. In: Gandon F, Cabrio E, Stankovic M, and Zimmermann A, editors. Springer; 2015. p. 234–243.

  43. Riloff E, Patwardhan S, Wiebe J. Feature subsumption for opinion analysis. EMNLP; 2006. p. 440–448.

  44. Wilson T, Wiebe J, Hwa R. Recognizing strong and weak opinion clauses. Comput Intell 2006;22(2): 73–99.

    Article  Google Scholar 

  45. Qi S u, Xinying X u, Guo H, Guo Z, Xian W u, Zhang X, Swen B, Zhong S u. Hidden sentiment association in chinese web opinion mining. WWW; 2008. p. 959–968.

  46. Jin W, Ho HH, Srihari RK. Opinionminer: a novel machine learning system for web opinion mining and extraction. KDD; 2009. p. 1195–1204.

  47. Jakob N, Gurevych I. Extracting opinion targets in a single and cross-domain setting with conditional random fields. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, 9-11 October 2010, MIT Stata Center, Massachusetts, USA, A meeting of SIGDAT, a Special Interest Group of the ACL, pp 1035–1045. ACL; 2010.

  48. Dragoni M, Tettamanzi Andrea GB, Pereira CC. Propagating and aggregating fuzzy polarities for concept-level sentiment analysis. Cogn Comput 2015;7(2):186–197.

    Article  Google Scholar 

  49. Dragoni M, Tettamanzi AGB, Pereira Célia da C. A fuzzy system for concept-level sentiment analysis. Valentina presutti, milan stankovic, erik cambria, iván cantador, angelo di iorio, tommaso di noia, christoph lange, diego reforgiato recupero, and anna tordai, editors, semantic web evaluation challenge - semwebeval 2014 at ESWC 2014, anissaras, crete, greece, may 25-29 (2014), revised selected papers, volume 475 of communications in computer and information science, pp 21–27. Springer; 2014.

  50. da Pereira CC, Dragoni M, Pasi G. A prioritized and aggregation operator for multidimensional relevance assessment. Roberto serra and rita cucchiara, editors, AI*IA 2009: Emergent perspectives in artificial intelligence, XIth international conference of the italian association for artificial intelligence, reggio emilia, italy, december 9-12, 2009, proceedings, volume 5883 of lecture notes in computer science, pp 72–81. Springer; 2009.

  51. Aprosio AP, Corcoglioniti F, Dragoni M, Rospocher M. Supervised opinion frames detection with RAID. Fabien gandon, elena cabrio, milan stankovic, and antoine zimmermann, editors, semantic web evaluation challenges - second semwebeval challenge at ESWC 2015, portorož, Slovenia, May 31 - June 4 (2015), Revised Selected Papers, volume 548 of Communications in Computer and Information Science, pp 251–263. Springer; 2015.

  52. Cambria E, Hussain A. Sentic computing: a common-sense-based framework for concept-level sentiment analysis. Cham Switzerland: Springer; 2015.

    Book  Google Scholar 

  53. Wang QF, Cambria E, Liu CL, Hussain A. Common sense knowledge for handwritten chinese recognition. Cogn Comput 2013;5(2):234–242.

    Article  Google Scholar 

  54. Cambria E, Hussain A. Sentic album: content-, concept-, and context-based online personal photo management system. Cogn Comput 2012;4(4):477–496.

    Article  Google Scholar 

  55. Gangemi A, Presutti V, Recupero DR. Frame-based detection of opinion holders and topics: a model and a tool. IEEE Comp Int Mag 2014;9(1):20–30.

    Article  Google Scholar 

  56. Recupero DR, Presutti V, Consoli S, Gangemi A, Sentilo Andrea Giovanni Nuzzolese. Frame-based sentiment analysis. Cogn Comput 2015;7(2):211–225.

    Article  Google Scholar 

  57. Thelwall M, Buckley K, Paltoglou G, Di C, Kappas A. Sentiment in short strength detection informal text. JASIST 2010;61(12):2544–2558.

    Article  Google Scholar 

  58. Fan Teng-Kai, Chang C-H. Sentiment-oriented contextual advertising. Inf Knowl Syst 2010;23(3):321–344.

    Article  Google Scholar 

  59. Dragoni M. A three-phase approach for exploiting opinion mining in computational advertising. IEEE Intell Syst 2017;32(3):21– 27.

    Article  Google Scholar 

  60. Sklar M, Concepcion KJ. Timely tip selection for foursquare recommendations. Poster proceedings of the 8th ACM conference on recommender systems, RecSys 2014, Foster City, Silicon Valley, CA, USA, October 6-10 (2014), volume 1247 of CEUR Workshop Proceedings. CEUR-WS.org. In: Chen L and Mahmud J, editors; 2014.

  61. Choi Y, Cardie C. Hierarchical sequential learning for extracting opinions and their attributes. I. Proceedings of the ACL 2010 conference short papers, ACLShort’10, pp 269–274, Stroudsburg, PA. USA; 2010. Association for Computational Linguistics.

  62. Zhang M, Zhang Y, Vo Duy-Tin. Neural networks for open domain targeted sentiment. Proceedings of the 2015 conference on empirical methods in natural language processing, EMNLP 2015, Lisbon, Portugal, September 17-21 (2015), pp 612–621. The Association for Computational Linguistics. In: Màrquez L, Callison-Burch C, Su J, Pighin D, and Marton Y, editors; 2015.

  63. Mitchell M, Aguilar J, Wilson T, Durme BV. Open domain targeted sentiment. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, 18-21 October 2013, Grand Hyatt Seattle, Seattle, Washington, USA, A meeting of SIGDAT, a Special Interest Group of the ACL, pp 1643–1654. ACL; 2013.

  64. Liu B, Minqing H u, Cheng J. Opinion observer: analyzing and comparing opinions on the web. WWW; 2005. p. 342–351.

  65. Mei Q, Ling X u, Wondra M, Hang S u, Zhai CX. Topic sentiment mixture: modeling facets and opinions in weblogs. Proceedings of the 16th international conference on World Wide Web, WWW ’07, pp 171–180, New York, NY, USA. ACM; 2007.

  66. Titov I, McDonald R. A joint model of text and aspect ratings for sentiment summarization. PROC. ACL-08: HLT; 2008. p. 308–316.

  67. Li F, Huang M, Zhu X. Sentiment analysis with global topics and local dependency. Proceedings of the twenty-fourth AAAI conference on artificial intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15, 2010; 2010.

  68. Mukherjee A, Liu CX. Aspect extraction through semi-supervised modeling. Proceedings of the 50th annual meeting of the association for computational linguistics: long papers - Volume 1, ACL’12, pp 339–348, Stroudsburg, PA, USA. Association for Computational Linguistics; 2012.

  69. Dragoni M, Azzini A, Tettamanzi A. A novel similarity-based crossover for artificial neural network evolution. Parallel problem solving from nature - PPSN XI, 11th International Conference, Krakȯw, Poland, September 11-15, 2010, Proceedings, Part I, volume 6238 of Lecture Notes in Computer Science, pp 344–353. Springer. In: Schaefer R, Cotta C, Kolodziej J, and Rudolph G, editors; 2010.

  70. Yuanbin W u, Qi Z, Huang X, Wu L. Phrase dependency parsing for opinion mining. Proceedings of the 2009 conference on empirical methods in natural language processing: volume 3 - Volume 3, EMNLP ’09, pp 1533–1541, Stroudsburg, PA, USA. Association for Computational Linguistics; 2009.

  71. Banko M, Cafarella M, Soderland S, Broadhead M, Etzioni O. Open information extraction for the web. Proceedings of the international joint conference on artificial intelligence, IJCAI ’07; 2007.

  72. Yates A, Cafarella M, Banko M, Etzioni O, Broadhead M, Textrunner Stephen Soderland. Open information extraction on the web. Proceedings of human language technologies: the annual conference of the North American chapter of the association for computational linguistics: demonstrations, NAACL-demonstrations ’07, pp 25–26, Stroudsburg, PA, USA. Association for Computational Linguistics; 2007.

  73. Wu F, Weld DS. Open information extraction using wikipedia. Proceedings of the 48th annual meeting of the association for computational linguistics, ACL’10, pp 118–127, Stroudsburg, PA, USA. Association for Computational Linguistics; 2010.

  74. Fader A, Soderland S, Etzioni O. Identifying relations for open information extraction. Proceedings of the conference on empirical methods in natural language processing, EMNLP’11, pp 1535–1545, Stroudsburg, PA, USA. Association for Computational Linguistics; 2011.

  75. Akbik A, Löser A. Kraken: N-ary facts in open information extraction. Proceedings of the joint workshop on automatic knowledge base construction and web-scale knowledge extraction, AKBC-WEKEX’12, pp 52–56, Stroudsburg, PA, USA. Association for Computational Linguistics; 2012.

  76. Mausam MS, Bart R, Soderland S, Etzioni O. Open language learning for information extraction. Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, EMNLP-coNLL’12, pp 523–534, Stroudsburg, PA, USA. Association for Computational Linguistics; 2012.

  77. Corro LD, Gemulla R. Clausie: clause-based open information extraction. Proceedings of the 22Nd international conference on World Wide Web, WWW’13, pp 355–366, New York, NY, USA. ACM; 2013.

  78. Bast H, Haussmann E. Open information extraction via contextual sentence decomposition. Proceedings of the 2013 IEEE 7th international conference on semantic computing, ICSC’13. IEEE; 2013.

  79. Zhila A, Gelbukh A. Open information extraction for spanish language based on syntactic constraints. ACL (Student research workshop); 2014. p. 78–85.

  80. Wang M, Li L, Huang F. Semi-supervised chinese open entity relation extraction. Proceedings of the 3rd IEEE international conference on cloud computing and intelligence systems. IEEE; 2014.

  81. Falke T, Stanovsky G, Gurevych I, Dagan I. Porting an open information extraction system from english to german. EMNLP; 2016. p. 892–898.

  82. Gamallo P, Garcia M, Fern’andez-Lanza S. Dependency-based open information extraction. EACL; 2012.

  83. Gamallo P, Garcia M. Multilingual open information extraction. Cham: Springer; 2015, pp. 711–722.

    Google Scholar 

  84. Fellbaum C. WordNet: an electronic lexical database. Cambridge: MIT Press; 1998.

    Book  Google Scholar 

  85. Cambria E, Poria S, Hazarika D, Kwok K. SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. AAAI; 2018. p. 1795–1802.

  86. Stone P, Dunphy DC, Marshall S. The general inquirer: a computer approach to content analysis. Oxford: MIT Press; 1966.

    Google Scholar 

  87. Deng L, Wiebe J. MPQA 3.0: an entity/event-level sentiment corpus. Rada mihalcea, joyce yue chai, and anoop sarkar, editors, NAACL HLT 2015, the 2015 conference of the north american chapter of the association for computational linguistics: human language technologies, denver, colorado, USA, May 31 - June 5 (2015), pp 1323–1328. The Association for Computational Linguistics; 2015.

  88. Poria S, Gelbukh A, Cambria E, Yang P, Hussain A, Durrani T. Merging senticnet and wordnet-affect emotion lists for sentiment analysis. Signal processing (ICSP) (2012) IEEE 11th international conference on, volume 2, pp 1251–1255. IEEE; 2012.

  89. Quirk R, Greenbaum S, Leech G, Svartvik J, Crystal D. A comprehensive grammar of the English language, volume 397. Cambridge: Cambridge Univ Press; 1985.

    Google Scholar 

  90. Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D. The stanford coreNLP natural language processing toolkit. Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, pp 55–60, Baltimore, Maryland. Association for Computational Linguistics; June 2014.

  91. Toutanova CMK, Klein D, Singer Y. Feature-rich part-of-speech tagging with a cyclic dependency network. Proceedings of HLT-NAACL 2003, pp 252–259; 2003.

  92. Clark K, Manning CD. Entity-centric coreference resolution with model stacking. Association for computational linguistics (ACL); 2015.

  93. Erhard W. 2003. Hinrichs and Dan Roth, editors. Accurate Unlexicalized Parsing.

  94. Chen D, Manning CD. A fast and accurate dependency parser using neural networks. Empirical methods in natural language processing (EMNLP); 2014.

  95. Liu Q, Gao Z, Liu B, Zhang Y. Automated rule selection for aspect extraction in opinion mining. Proceedings of the twenty-fourth international joint conference on artificial intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31 (2015), pp 1291–1297. AAAI Press. In: Yang Q and Wooldridge M, editors; 2015.

Download references

Author information

Authors and Affiliations

  1. Fondazione Bruno Kessler, Trento, Italy

    Mauro Dragoni

  2. University of Amsterdam, Amsterdam, The Netherlands

    Marco Federici

  3. Know-Center, Graz, Austria

    Andi Rexha

Authors
  1. Mauro Dragoni
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Marco Federici
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Andi Rexha
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Mauro Dragoni.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Informed Consent

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

Human and Animal Rights

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

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

Dragoni, M., Federici, M. & Rexha, A. ReUS: a Real-time Unsupervised System For Monitoring Opinion Streams. Cogn Comput 11, 469–488 (2019). https://doi.org/10.1007/s12559-019-9625-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-019-9625-x

Keywords