 - Text classification models built on features like bag of words give high accuracy when predictive words appear both in training and test data.
 - Data sparsity


San Jose State University

Surface level features in text classification tasks are 

 - Bag of words
 - Unigrams( Word level)
 - Large n-grams(Word Level)
 - N-grams combined with other features 
 - Character n-grams
 - Frequency of URL mentions and punctuations
 - Comment and token length 
 - Capitalization
 - Words not in English dictionaries 
 - Number of non-alpha numeric characters present in tokens 


Simple Surface Features Needed for Text Classification Tasks

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

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

A Survey on Hate Speech Detection using Natural Language Processing

Consider the phrase “$ ki11 yrslef a$$hole$” an example of hate speech , token based models/systems does not recognise this as a hate speech so features based on character level n-gram is in need. 


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