Consider the example " Jews are lower class pigs."

a dependency tuple nsubj(pigs, Jews) will denote the relation between the offensive term pigs and the hate-target Jews.


San Jose State University

 - Combining n-gram features with POS-information-enriched tokens, but classifier performance was not increased significantly.
 - Deep syntactic information( long distance relation between words in a sentence).

 - Dependency relationships in a sentence can be used as a feature which increases performance of the classification tasks.
 - Dependency relations are selected manually or using Bayesian Logistic Regression
Some researchers have used an offensive score which is calculated based on  the number of times an offensive word repeats in a sentence or passage
 - In an approach called Smokey system researcher uses specific linguistic and syntactic features to detect hate speech. In addition to this, praise rules and politeness rules are implemented to detect co-occurence of good words.
 - For example imperative statements like imperative statements (e.g. Get lost!,Get a life!)
the co occurrence of the pronoun you modified by noun phrases (as in you bozos)



Linguistic Features For Hate Speech Detection

A Survey on Hate Speech Detection using Natural Language Processing by Anna Schmidt  and Michael Wiegand

https://aclanthology.org/W17-1101.pdf

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