Is it possible to use use fuzzy classification models such as fknn, fsvm in nlp? I mean I've seen people use K-nn, SVM over textual feature datas extracted from twitter/reddit api to detect emotions like depression,, suicide etc. Can we use fuzzy Knn, fuzzy svm for such emotion detection over textual features too? I haven't seen any research paper regarding this issue or anyone who used fKnn or any fuzzy classification model on textual features/social media data to detect emotions. Can anybody explain its answer in details to me?
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I think the first question you may need to ask is what does a "fuzzy decision" in language mean? The objective of language at its base requirement is to convey a clear message that on one scale means something definitive. For example, if you want to express that you are happy, you say "I am happy" and NOT "I may be happy".
In language processing, it is even difficult to extract intentions and meanings in a simple form using traditional and sophisticated algorithms. For other domains, there are use cases where fuzzy outcomes are useful. But what does that mean for language? This is one obvious reason you may not see any literature with fuzzy logic applied to NLP.
Arun Aniyan
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Thank you, now i understand the issue better. – Aveiro11 Sep 26 '22 at 15:04