A Knowledge-Based Weighted KNN for Detecting Irony in Twitter
[EN] In this work, we propose a variant of a well-known instancebased 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 betwe...
| Authors: | , , , , |
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| Format: | article |
| Publication Date: | 2018 |
| Country: | España |
| Institution: | Universitat Politècnica de València (UPV) |
| Repository: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Language: | English |
| OAI Identifier: | oai:riunet.upv.es:10251/146278 |
| Online Access: | https://riunet.upv.es/handle/10251/146278 |
| Access Level: | Open access |
| Keyword: | Instance-based algorithm WKNN Irony detection LENGUAJES Y SISTEMAS INFORMATICOS |
| Summary: | [EN] In this work, we propose a variant of a well-known instancebased 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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