Abstract
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.
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
References
Alpaydin, E.: Introduction to Machine Learning, MIT Press, Cambridge (2020)
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)
Bengio, Y., Ducharme, R., Vincent, P., Jauvin C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 11371155 (2003)
Bouazizi, M., Otsuki, T.: A pattern-based approach for sarcasm detection on Twitter. IEEE Access 4, 5477–5488. cited By 7 (2016)
Statistical comparisons of classifiers over multiple data sets: Dems̃ar. J. Mach. Learn. Res. 7, 1–30 (2006)
Li, P., Farkas, I.: Early lexical development in a self-organizing neural network. Neural Netw. 17(8–8), 1345–1362 (2004)
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)
Miikkulainen, R., Dyer, M.: Natural language processing with modular PDP networks and distributed lexicon. Cognitive Science (1991)
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)
Mullen, T., Collier, N.: Sentiment Analysis using Support Vector Machines with Diverse Information Sources. Publicado en EMNLP (2004)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)
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)
Sharma, A.: Dey S.: Using Self-Organizing Maps for Sentiment Analysis. CoRR abs1309.3946 (2013)
Sun, S., Luo, C., Chen, J.: A review of natural language processing techniques for opinion mining systems. Inf. Fusion 36, 10–25 (2017)
Whitelaw, C., Garg, N., Argamon, S.E.: Using appraisal groups for sentiment analysis. CIKM (2005)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
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. https://doi.org/10.1007/978-3-030-87687-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-87687-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87686-9
Online ISBN: 978-3-030-87687-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)