Skip to main content

Opinion Mining for Curriculum Enrichment Using Self-Organizing Maps

  • 403 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1410)


Recently social networks have become a valuable source of information where tastes, preferences and opinions of users around the world come together. This information is an interesting challenge from the perspective of natural language processing (NLP) but is also an aspect of deep interest and great value not only as a marketing strategy for companies and political campaigns but also as an indicator for measuring consumer satisfaction with a product or service. In this paper, we present an opinion mining system that uses text mining techniques and artificial neural networks to automatically obtain useful knowledge about opinions, preferences and user trends. Making use of the Self-Organizing Maps (SOM), we train a neural network that is capable of depending on what is expressed by users in social networks, discern their mood, tastes and experiences in order to help a personnel selection company to find customers and employees necessities. The analysis of these results will make it possible to undertake corrective actions to improve the opinion of the user in relation to their work development. In all experiments, using SOM, we achieve a quantization error below 0.02. In addition, taking into account the evaluation metrics, It can be said that the model has been able to learn and relate the input context values and the results, which proves that the training has been successful and therefore the classification.


  • Sentiment analysis
  • Opinion mining
  • Social networks
  • Natural language processing
  • Text mining
  • Twitter
  • Self-organizing Maps

Sistemas de monitorización y seguimiento para la mejora de la movilidad inteligente y el análisis de comportamiento (SiMoMIAC). PID2019-108883RB-C21. MINISTERIO DE CIENCIA, INNOVACIÓN Y UNIVERSIDADES.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-87687-6_9
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   149.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-87687-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   199.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.


  1. 1.

  2. 2.

  3. 3.

  4. 4.

  5. 5.

  6. 6.

  7. 7.

  8. 8.

  9. 9.

  10. 10.


  1. Alpaydin, E.: Introduction to Machine Learning, MIT Press, Cambridge (2020)

    Google Scholar 

  2. Appel, O., Chiclana, F., Carter, J., Fujita, H.: A hybrid approach to the sentiment analysis problem at the sentence level. Knowl.-Based Syst.108, 110–124 (2016). New Avenues in Knowledge Bases for Natural Language Processing (2016)

    Google Scholar 

  3. Bengio, Y., Ducharme, R., Vincent, P., Jauvin C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 11371155 (2003)

    Google Scholar 

  4. Bouazizi, M., Otsuki, T.: A pattern-based approach for sarcasm detection on Twitter. IEEE Access 4, 5477–5488. cited By 7 (2016)

    Google Scholar 

  5. Statistical comparisons of classifiers over multiple data sets: Dems̃ar. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  Google Scholar 

  6. Li, P., Farkas, I.: Early lexical development in a self-organizing neural network. Neural Netw. 17(8–8), 1345–1362 (2004)

    CrossRef  Google Scholar 

  7. López, V.F., Corchado, J.M., De Paz, J.F., Rodríguez, S., Bajo J.A: SomAgent statistical machine translation. Appl. Soft Comput. 11(2), 2925–2933 (2011)

    Google Scholar 

  8. Miikkulainen, R., Dyer, M.: Natural language processing with modular PDP networks and distributed lexicon. Cognitive Science (1991)

    Google Scholar 

  9. Moraes, R., Valiati, J.F., Gavião, W.P.: Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst. Appl. 40, 621–33 (2013)

    CrossRef  Google Scholar 

  10. Mullen, T., Collier, N.: Sentiment Analysis using Support Vector Machines with Diverse Information Sources. Publicado en EMNLP (2004)

    Google Scholar 

  11. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)

    CrossRef  Google Scholar 

  12. Rumelhart,D., McClelland, J.: On learning the past tenses of English verbs. In: Parallel distributed processing; Volume 2: Psychological and Biological Models, pp. 216-271. MIT Press, Cambridge (1986)

    Google Scholar 

  13. Sharma, A.: Dey S.: Using Self-Organizing Maps for Sentiment Analysis. CoRR abs1309.3946 (2013)

    Google Scholar 

  14. Sun, S., Luo, C., Chen, J.: A review of natural language processing techniques for opinion mining systems. Inf. Fusion 36, 10–25 (2017)

    CrossRef  Google Scholar 

  15. Whitelaw, C., Garg, N., Argamon, S.E.: Using appraisal groups for sentiment analysis. CIKM (2005)

    Google Scholar 

  16. Tikkala, A., Eikmeyer, H.J., Niemi, J., Laine, M.: The Production of finish nouns: a psycholinguistically motivated connectionist model. Connect. Sci. 9(3), 295–314 (1997)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Edgard Naranjo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Naranjo, E., López, V.F., Moreno, M.N., Muñoz, M.D., Martín, J.J.S. (2022). Opinion Mining for Curriculum Enrichment Using Self-Organizing Maps. In: de Paz Santana, J.F., de la Iglesia, D.H., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. DiTTEt 2021. Advances in Intelligent Systems and Computing, vol 1410. Springer, Cham.

Download citation