Abstract
In this work, we propose a variant of a well-known instance-based algorithm: WKNN. Our idea is to exploit task-dependent features in order to calculate the weight of the instances according to a novel paradigm: the Textual Attraction Force, that serves to quantify the degree of relatedness between documents. The proposed method was applied to a challenging text classification task: irony detection. We experimented with corpora in the state of the art. The obtained results show that despite being a simple approach, our method is competitive with respect to more advanced techniques.
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Notes
- 1.
Where \(\partial (c, f(knn_i))\) returns 1 if \(c=f(knn_i)\) and 0 otherwise.
- 2.
This example is part of the obtained results when the aforementioned method was applied with a size of k = 7. All the tweets were extracted from dataset developed by [22].
- 3.
This tweet was labeled with the class “Irony”.
- 4.
The real classes of these tweets are denoted as follows: “*” represents the class “Irony” while “**” is denoted as “Nonirony”.
- 5.
Hashtags, mentions, emoji, and url were not considered in the bag-of-words model.
- 6.
We consider five different punctuation marks: “.”, “,”, “:”, “!”, and “?”.
- 7.
We used a list of terms defined in https://en.wiktionary.org/wiki/Appendix:English_internet_slang.
- 8.
We used the embeddings pre-trained on the Google News corpus.
- 9.
Satire is strongly related to verbal irony, providing a detailed definition of such a concept is beyond of the scope of this work.
- 10.
In computational linguistics, irony is often considered as an umbrella term that covers also sarcasm.
- 11.
We performed three different binary classifications by combining each of the nonironic classes with the ironic one. From now on, these experiments will be referred as TwReyes2013-Edu, TwReyes2013-Hum, and TwReyes2013-Pol.
- 12.
- 13.
Where massFunction can be any of the functions defined in Sect. 3.1.
- 14.
The default configuration of parameters in the classifiers was applied.
- 15.
The authors reported the performance of their model in terms of F-measure considering both classes together. Attempting to compare our results, we carried out experiments by exploiting the aforementioned model but instead of considering an overall performance, we are reporting only the performance in terms of the ironic class.
- 16.
For further details on the shared task see [22].
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Acknowledgments
This research was funded by CONACYT project FC 2016-2410. The work of P. Rosso has been funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project. The work of V. Patti was partially funded by Progetto di Ateneo/CSP 2016 (IhatePrejudice, S1618_L2_BOSC_01).
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Hernández Farías, D.I., Montes-y-Gómez, M., Escalante, H.J., Rosso, P., Patti, V. (2018). A Knowledge-Based Weighted KNN for Detecting Irony in Twitter. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Computational Intelligence. MICAI 2018. Lecture Notes in Computer Science(), vol 11289. Springer, Cham. https://doi.org/10.1007/978-3-030-04497-8_16
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