Métodos de Procesado del Lenguaje Natural aplicados al estudio de las coberturas mediáticas

• Castillo-Campos, Mar ^[1] ; Becerra-Alonso, David ^[1] ; Varona-Aramburu, David ^[2]
  1. [1] Universidad Loyola Andalucía

     Universidad Loyola Andalucía

     Sevilla, España

     
  2. [2] Universidad Complutense de Madrid

     Universidad Complutense de Madrid

     Madrid, España

     
• Localización: Comunicación & métodos, ISSN-e 2659-9538, Vol. 4, Nº. 2, 2022 (Ejemplar dedicado a: La relevancia del método), págs. 85-99
• Idioma: español
• DOI: 10.35951/v4i2.171
• Títulos paralelos:
  • Natural Language Processing Methods Applied to the Study of Media Coverage
• Enlaces
  • Texto completo
• Resumen
  • español

    El Procesamiento del Lenguaje Natural comprende distintas técnicas cuantitativas para el análisis de textos y, aunque de probada solvencia, aún es infrecuente en el estudio del periodismo. La propuesta metodológica de esta investigación se ha diseñado para el análisis de la cobertura en medios de comunicación de las elecciones a la Asamblea de Madrid celebradas en 2021, y se desarrolla en tres fases: conteo de términos, estudio de relación entre binomios de conceptos mediante redes neuronales y agrupación y proyección de términos. Los resultados se han comparado con estudios previos de cobertura mediática realizados con otros métodos. Esta investigación muestra que la mecanización y la automatización de las técnicas propuestas es eficiente y sirve además como punto de partida para investigaciones cualitativas o mixtas que exploran textos en profundidad. La flexibilidad del método permite además experimentar con distintos grupos de palabras de medios de comunicación o cualquier otra fuente documental. 

  • English

    Natural Language Processing comprises different quantitative techniques for analysing texts and, although of proven solvency, it is still infrequent in the study of journalism. The methodological proposal of this research has been designed for the analysis of the media coverage of the elections to the Assembly of Madrid held in 2021. It is developed in three phases: counting of terms, studying the relationship between concepts using neural networks, and clustering and projection of terms. The results have been compared with previous studies of media coverage carried out with other methodologies. This research shows that the mechanization and automation of the proposed techniques are efficient for comparison, and serve as a starting point for qualitative or mixed research that explores texts in depth. The flexibility of the method also allows experimentation with different groups of words from media or any other documentary source.

• Referencias bibliográficas
  • Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python. O'Reilly Media.
  • Berven, A., Christensen, O., Moldeklev, S., Opdahl, A., & Villanger, K., (2020). A knowledge-graph platform for newsrooms. Computers in...
  • Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C., & Jatowt, A. (2020). YAKE! Keyword extraction from single documents using...
  • Casero-Ripollés, A., Feenstra, R., & Tormey, S. (2016). Old and New Media Logics in an Electoral Campaign The Case of Podemos and the...
  • Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification:...
  • Christian, H., Agus, M. P., & Suhartono, D. (2016). Single document automatic text summarization using term frequency-inverse document...
  • Doan, S., Yang, E. W., Tilak, S. S., Li, P. W., Zisook, D. S., & Torii, M. (2019). Extracting health-related causality from twitter messages...
  • Edell, A. (2018). I trained fake news detection AI with >95% accuracy, and almost went crazy. En Towards Data Science. https://towardsdatascience.com/i-trained-fake-news-detection-ai-with-95-accuracy-and-almost-went-crazy-d10589aa57c
  • Emadi, M., & Rahgozar, M. (2020). Twitter sentiment analysis using fuzzy integral classifier fusion. Journal of Information Science, 46(2),...
  • Fenoll, V., & Rodríguez-Ballesteros, P. (2017). Análisis automatizado de encuadres mediáticos. Cobertura en prensa del debate 7D 2015:...
  • Gao, Z., Feng, A., Song, X., & Wu, X. (2019). Target-dependent sentiment classification with BERT. IEEE Access 7, 154290-154299. https://10.1109/ACCESS.2019.2946594
  • García-Marín, J., Calatrava García, A., & Luengo, Ó. G. (2018). Debates electorales y conflicto. Un análisis con máquinas de soporte virtual...
  • Goularas, D., & Kamis, S. (2019, August). Evaluation of deep learning techniques in sentiment analysis from twitter data. In 2019 International...
  • Iyengar, S., & Simon, A. F. (2000). New perspectives and evidence on political communication and campaign effects. Annual review of psychology,...
  • Jang, B., Kim, I., & Kim, J. (2019). Word2vec convolutional neural networks for classification of news articles and tweets. PloS one,...
  • Jung, N., & Lee, G. (2019). Automated classification of building information modeling (BIM) case studies by BIM use based on natural language...
  • Kim, D., Seo, D., Cho, S., & Kang, P. (2019). Multi-co-training for document classification using various document representations: TF–IDF,...
  • Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: A survey....
  • Kuncoro, B.A., & Iswanto, B.H. (2015, November). TF-IDF method in ranking keywords of Instagram users' image captions. En 2015 International...
  • Labio-Bernal, A. (2018). Anti-communism and the mainstream online press in Spain: Criticism of Podemos as a strategy of a two-party system...
  • Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. In International conference on machine learning,...
  • Li, L., Johnson, J., Aarhus, W., & Shah, D. (2022). Key factors in MOOC pedagogy based on NLP sentiment analysis of learner reviews: What...
  • Mancera-Rueda, A., & Villar-Hernández, P. (2020). Análisis de las estrategias de encuadre discursivo en la cobertura electoral sobre Vox...
  • Marshall, M. N. (1996). Sampling for qualitative research. Family practice, 13(6), 522-526. https://doi.org/10.1093/fampra/13.6.522
  • McInnes, L., Healy, J., & Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint...
  • McNair, B. (2017). An introduction to political communication. Routledge.
  • Miguel-Sáez-de-Urabain, A., Fernández-de-Arroyabe-Olaortua, A., & Lazkano-Arrillaga, I. (2017). La espectacularización de la información...
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781
  • Müller, M., Salathé, M., & Kummervold, P. E. (2020). Covid-twitter-bert: A natural language processing model to analyse covid-19 content...
  • Paniagua-Rojano, F., Seoane-Pérez, F., & Magallón-Rosa, R. (2020). Anatomía del bulo electoral: la desinformación política durante...
  • Qaiser, S., & Ali, R. (2018). Text mining: use of TF-IDF to examine the relevance of words to documents. International Journal of Computer...
  • Salton, G., Buckley, C. (1988). Term-Weighting approaches in Automatic Text Retrieval. Information Processing and Management, 24(5), 513–523....
  • Sánchez Gutiérrez, B. (2016). La representación mediática de los partidos políticos emergentes: el caso de Podemos y Ciudadanos en Atresmedia...
  • Sánchez-Gutiérrez, B., & Nogales-Bocio, A. I. (2018). La cobertura mediática de Podemos en la prensa nativa digital neoliberal española:...
  • Shapiro, A. H., Sudhof, M., & Wilson, D. (2020). Measuring news sentiment. Journal of Econometrics 228(2), 221-243. https://doi.org/10.1016/j.jeconom.2020.07.053
  • Singh, K., Sen, I., & Kumaraguru, P. (2018, July). A Twitter corpus for Hindi-English code mixed POS tagging. En Proceedings of the sixth...
  • Sun, S., Luo, C., & Chen, J. (2017). A review of natural language processing techniques for opinion mining systems. Information fusion,...
  • Thavareesan, S., & Mahesan, S. (2020, July). Sentiment lexicon expansion using Word2vec and fastText for sentiment prediction in Tamil...
  • Tian, X., & Tong, W. (2010). An improvement to TF: Term distribution based term weight algorithm. En 2010 Second International Conference...
  • Xia, T., & Chai, Y. (2011). An Improvement to TF-IDF: Term Distribution based Term Weight Algorithm. Journal of Software, 6(3), 413-420....
  • Vermeulen, M., Smith, K., Eremin, K., Rayner, G., & Walton, M. (2021). Application of Uniform Manifold Approximation and Projection (UMAP)...
  • Wongso, R., Luwinda, F. A., Trisnajaya, B. C., & Rusli, O. (2017). News article text classification in Indonesian language. Procedia Computer...
  • Zhou, P., Shi, W., Zhao, J., Huang, K-H., Chen, M., & Chang, K-W. (2019). Analyzing and Mitigating Gender Bias in Languages with Grammatical...