Cognitive Computation

, Volume 11, Issue 5, pp 656–675 | Cite as

Cognitive Insights into Sentic Spaces Using Principal Paths

  • Edoardo RagusaEmail author
  • Paolo Gastaldo
  • Rodolfo Zunino
  • Marco Jacopo Ferrarotti
  • Walter Rocchia
  • Sergio Decherchi


The availability of an effective embedding to represent textual information is important in commonsense reasoning. Assessing the quality of an embedding is challenging. In most approaches, embeddings are built using statistical properties of the data that are not directly interpretable by a human user. Numerical methods can be inconsistent with respect to the target problem from a cognitive view point. This paper addresses the issue by developing a protocol for evaluating the coherence between an embedding space and a given cognitive model. The protocol uses the recently introduced notion of principal path, which can support the exploration of a high-dimensional space. The protocol provides a qualitative measure of concept distributions in a graphical format, which allows the embedding properties to be analyzed. As a consequence, the tool mitigates the black-box effect that is typical of automatic inference processes. The experimental section involves the characterization of AffectiveSpace, demonstrating that the proposed approach can be used to describe embeddings. The reference cognitive model is the hourglass model of emotions.


Topological analysis Sentiment analysis Affective computing Concept embedding 


Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Informed Consent

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

Human and Animal Rights

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


  1. 1.
    Cambria E, Poria S, Gelbukh A, Thelwall M. Sentiment analysis is a big suitcase. IEEE Intell Syst 2017;32(6):74–80.CrossRefGoogle Scholar
  2. 2.
    Li Y, Pan Q, Yang T, Wang S, Tang J, Cambria E. Learning word representations for sentiment analysis. Cogn Comput 2017;9(6):843–851.CrossRefGoogle Scholar
  3. 3.
    Ofek N, Poria S, Rokach L, Cambria E, Hussain A, Shabtai A. Unsupervised commonsense knowledge enrichment for domain-specific sentiment analysis. Cogn Comput 2016;8(3):467–477.CrossRefGoogle Scholar
  4. 4.
    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):639–650.CrossRefGoogle Scholar
  5. 5.
    Yang H-C, Lee C-H, Wu C-Y. Sentiment discovery of social messages using self-organizing maps. Cogn Comput 2018;10(6):1152–1166.CrossRefGoogle Scholar
  6. 6.
    Peng H, Cambria E, Hussain A. A review of sentiment analysis research in chinese language. Cogn Comput 2017;9(4):423–435.CrossRefGoogle Scholar
  7. 7.
    Bengio Y, Ducharme R, Vincent P, Jauvin C. A neural probabilistic language model. J Mach Learn Res 2003;3(Feb):1137–1155.Google Scholar
  8. 8.
    Collobert R, Weston J. A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning. ACM; 2008. p. 160–167.Google Scholar
  9. 9.
    Huang EH, Socher R, Manning CD, Ng AY. Improving word representations via global context and multiple word prototypes. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, Association for Computational Linguistics; 2012, p. 873–882.Google Scholar
  10. 10.
    Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space, arXiv:1301.3781.
  11. 11.
    Mnih A, Hinton G. Three new graphical models for statistical language modelling. In: Proceedings of the 24th International Conference on Machine Learning. ACM; 2007. p. 641–648.Google Scholar
  12. 12.
    Tang J, Qu M, Mei Q. Pte: Predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; 2015, p. 1165–1174.Google Scholar
  13. 13.
    Wang S, Tang J, Aggarwal C, Liu H. Linked document embedding for classification. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM; 2016. p. 115–124.Google Scholar
  14. 14.
    Ma Y, Peng H, Cambria E. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: AAAI; 2018. p. 5876–5883.Google Scholar
  15. 15.
    Pennington J, Socher R, Manning CD. Glove: Global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP); 2014. p. 1532–1543.
  16. 16.
    Wilson T, Wiebe J, Hoffmann P. Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Association for Computational Linguistics; 2005. p. 347–354.Google Scholar
  17. 17.
    Mohammad SM, Turney PD. Crowdsourcing a word–emotion association lexicon. Comput Intell 2013;29 (3):436–465.CrossRefGoogle Scholar
  18. 18.
    Cambria E, Poria S, Hazarika D, Kwok K. SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In: AAAI; 2018. p. 1795–1802.Google Scholar
  19. 19.
    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.CrossRefGoogle Scholar
  20. 20.
    Cambria E, Fu J, Bisio F, Poria S. Affectivespace 2: Enabling affective intuition for concept-level sentiment analysis.. In: AAAI; 2015. p. 508–514.Google Scholar
  21. 21.
    Carlsson G. Topology and data. Bull Am Math Soc 2009;46(2):255–308.CrossRefGoogle Scholar
  22. 22.
    Pearson K. Liii. on lines and planes of closest fit to systems of points in space. Lond Edinb Dublin Philos Mag J Sci 1901;2(11):559–572.CrossRefGoogle Scholar
  23. 23.
    Schölkopf B, Smola A, Müller K-R. Kernel principal component analysis. In: International Conference on Artificial Neural Networks. Springer; 1997. p. 583–588.Google Scholar
  24. 24.
    Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding. Science 2000;290 (5500):2323–2326.CrossRefGoogle Scholar
  25. 25.
    Kruskal JB. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 1964;29(1):1–27.CrossRefGoogle Scholar
  26. 26.
    Maaten Lvd, Hinton G. Visualizing data using t-sne. J Mach Learn Res 2008;9(Nov):2579–2605.Google Scholar
  27. 27.
    Liu S, Maljovec D, Wang B, Bremer P-T, Pascucci V. Visualizing high-dimensional data: Advances in the past decade. IEEE Trans Vis Comput Graph 2017;23(3):1249–1268.PubMedCrossRefGoogle Scholar
  28. 28.
    Ragusa E, Gastaldo P, Zunino R, Cambria E. Learning with similarity functions: a tensor-based framework. Cogn Comput 2019;11(1):31–49.CrossRefGoogle Scholar
  29. 29.
    Peng X, Selvachandran G. Pythagorean fuzzy set: state of the art and future directions. Artif Intell Rev. 2017:1–55.Google Scholar
  30. 30.
    Ferrarotti MJ, Rocchia W, Decherchi S. Finding principal paths in data space. IEEE Transactions on Neural Networks and Learning Systems. 2018:1–14. Scholar
  31. 31.
    Hastie T, Stuetzle W. Principal curves. J Am Stat Assoc 1989;84(406):502–516.CrossRefGoogle Scholar
  32. 32.
    Plutchik R. The nature of emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am Sci 2001;89(4):344–350.CrossRefGoogle Scholar
  33. 33.
    Cambria E, Livingstone A, Hussain A. The hourglass of emotions. In: Cognitive Behavioural Systems. Springer; 2012. p. 144–157.Google Scholar
  34. 34.
    Liu H, Singh P. Conceptnet—a practical commonsense reasoning tool-kit. BT Technol J 2004;22(4):211–226.CrossRefGoogle Scholar
  35. 35.
    Strapparava C, Valitutti A, et al. Wordnet affect: an affective extension of wordnet. In: Lrec, Vol. 4, Citeseer; 2004. p. 1083–1086.Google Scholar
  36. 36.
    Cambria E, Poria S, Bajpai R, Schuller B. SenticNet 4: A semantic resource for sentiment analysis based on conceptual primitives. In: COLING; 2016. p. 2666–2677.Google Scholar
  37. 37.
    Cambria E, Hussain A. Sentic computing: a Common-Sense-Based framework for Concept-Level sentiment analysis. Cham: Springer; 2015.CrossRefGoogle Scholar
  38. 38.
    Bottou L, Bengio Y. Convergence properties of the k-means algorithms. In: Advances in Neural Information Processing Systems; 1995. p. 585–592.Google Scholar
  39. 39.
    Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 2003;15(6):1373–1396.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical, Electronic and Telecommunications Engineering, and Naval Architecture, DITENUniversity of GenoaGenoaItaly
  2. 2.ConceptLabFondazione Istituto Italiano di TecnologiaGenoaItaly
  3. 3.Computational and Chemical BiologyFondazione Istituto Italiano di TecnologiaGenoaItaly

Personalised recommendations