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International Workshop on Soft Computing Models in Industrial and Environmental Applications

SOCO 2019: 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) pp 513–522Cite as

Opinion Mining to Detect Irony in Twitter Messages in Spanish

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 950))

Abstract

Companies, among other sectors, require that the opinions generated on the web be extracted automatically, obtaining their polarity on products or services, to achieve their objectives. Since the opinions are subjective and unstructured, there are still many problems within this field that must be solved. To mention a few, the problem of ambiguity and the support of languages, directly affect in the time to make the right classification of opinions, because most of the tools used in the processing of texts, they only work well with data in English. With the aim of contributing to the solution of both problems and evaluating the real behavior of sentiment analysis for the Spanish language, a system is proposed that allows determining the positive or negative polarity, trying to detect the irony as a problem of ambiguity. For the classification, a supervised learning method was implemented, with the Naive Bayes algorithm. The evaluation of the results of the classification shows that the problem of detecting ironies in Spanish, using the classical techniques of opinion mining, is not completely resolved. However, we believe that these results can be improved by applying some strategies.

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Notes

  1. 1.

    http://www.nltk.org/.

  2. 2.

    http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/.

  3. 3.

    List of words where each one has a numerical value that corresponds to the degree of sentiment whether positive or negative.

  4. 4.

    https://nlp.stanford.edu/software/tokenizer.html.

  5. 5.

    http://treetaggerwrapper.readthedocs.io/en/latest.

  6. 6.

    http://tweepy.readthedocs.io/en/v3.5.0/getting_started.html.

  7. 7.

    https://pandas.pydata.org/.

  8. 8.

    http://www.snowball.tartarus.org/.

  9. 9.

    http://scikit-learn.org/stable/index.html.

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Acknowledgments

This work has been supported by project “IOTEC: Development of Technological Capacities around the Industrial Application of Internet of Things (IoT)”. 0123_IOTEC_3_E. FEDER Funds. Interreg Spain-Portugal.

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Authors and Affiliations

  1. Departamento de Informática y Automática, Facultad de Ciencias, Universidad de Salamanca, Plaza de los Caídos s/n, 37008, Salamanca, Spain

    Daniela E. Sanjinés, Vivian F. López, Ana B. Gil & María N. Moreno

Authors
  1. Daniela E. Sanjinés
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  2. Vivian F. López
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  3. Ana B. Gil
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  4. María N. Moreno
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Corresponding author

Correspondence to Vivian F. López .

Editor information

Editors and Affiliations

  1. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  2. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  3. University of Salamanca, Salamanca, Spain

    José António Sáez Muñoz

  4. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Sanjinés, D.E., López, V.F., Gil, A.B., Moreno, M.N. (2020). Opinion Mining to Detect Irony in Twitter Messages in Spanish. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_49

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