# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 2 Background and Related Work
### Natural Language Processing (NLP) in Cyberbullying Detection  
One direction in this field is to detect the offensive content using Natural Language Processing (NLP). The most explanatory method for presenting what happens within a Natural Language Processing system is using the \"levels of language\" approach [42]. These levels are used by people to extract meaning from text or spoken languages. This levelling refers to the reason that language processing relies mainly on formal models or representations of knowledge related to these levels [42; 43]. Moreover, language processing applications distinguish themselves from data processing systems by using the knowledge of the language. The analysis of natural language processing has the following levels:  
* Phonology level (knowledge of linguistic sounds)
* Morphology level (knowledge of the meaningful components of words)
* Lexical level (deals with the lexical meaning of words and parts of speech analyses)
* Syntactic level (knowledge of the structural relationships between words)
* Semantic level (knowledge of meaning)
* Discourse level (knowledge about linguistic units more extensive than a single utterance)
* Pragmatic level (knowledge of the relationship of meaning to the goals and intentions of the speaker)  
[PERSON] et al. [44], for example, used a common-sense knowledge base with associated reasoning techniques. [PERSON] et al. [45] recognized cyberbullying content based on Formspring.me data, using query words used in cyberbullying cases. [PERSON] et al. [46] use several natural language processing methods to detect signs of bullying (a new term relating to online references that could be bullying instances themselves or online references relating to off-line bullying cases). They use sentiment analysis features to identify bullying roles and Latent Dirichlet Analysis to identify subjects/themes. The authors in [46] are intended to set the basis for several tasks relating to identifying bullying and providing a call for other researchers to enhance these specific techniques. Therefore, [PERSON] et al. [47]; [PERSON] et al. [48]; and [PERSON] et al. [44] are the earliest researchers working in NLP cyberbullying detection, who investigated predictive strength n-grams, part-speech information (e.g., first and second pronoun), and sentiment information based on profanity lexicons for this task (with and without TF-IDF weighting). Similar features were also used for detecting events related to cyberbullying and fine-grained categories of text in [49].  
To conclude, some of the common word representation techniques used and proven to improve the classification accuracy [50] are Term Frequency (TF) [51], Term Frequency-Inverse Document Frequency (TF-IDF) [52], Global Vectors for Word Representation (GloVe) [53], and Word2 Vec [54]. One of the main limitations of NLP is that of contextual expert knowledge. For instance, many dubious claims about the detection of sarcasm, but how one would detect sarcasm in a short post like \"Great game!\" responded to a defeat. Therefore, it is not about linguistics; it is about possessing knowledge relevant to the conversation.