# A Review on Deep-Learning-Based Cyberbullying Detection
## 9 Challenges, Open Issues, and Future Trends
### Challenges in Cyberbullying detection  
* Cultural diversity for cyberbullying: Language is one of the important parts of the culture of a nation. Since cyberbullying has become a common problem among different nations, we may not expect a good prediction model by using a dataset of one nation and testing over the dataset of another culturally varied nation.
* Language challenge: Capturing context and analyzing the sentiment from different types of sentences is a difficult task and challenging work for cyberbullying detection. For example, \"The image that you have sent so irritated me and I would rather not contact with you any longer!\" is not easy to detect as cyberbullying without investigating from a rationale factor, albeit that model shows negative sentiment [26].
* Dataset challenge: Retrieving data from social media is not an easy task, as it relates to private information. Moreover, social media sites do not share user data publicly. Due to these issues, it is hard to gather quality data from social sites, which causes the lack of quality data to improve learning. Another challenging task is to annotate or label the data because they require a domain expert to label the corpus [202].
* Data representation challenge: Setting up an effective cyberbullying-detection system is difficult due to the need for human interaction and the nature of cyberbullying. Furthermore, the nature of cyberbullying is challenging to identify in the cyberbullying detection problem. The vast majority of the exploratory works directly identified bullying words in social media. However, separating content-based features have their own difficulties. For the absence of appropriate information, the performance of the model might decay [203].
* Natural Language Processing (NLP) challenges: The biggest challenge in natural language processing is understanding the meaning of the text. The relevant task is to build the right vocabulary, link the various components of the vocabulary, establish context, and extract semantic meaning from the data [204]. Misspelling and ambiguous expressions are other challenges that are very difficult to solve for the machine.
* Reusability of pre-trained model for sentiment analysis and cyberbullying: Although cyberbullying detection and sentiment analysis are related tasks, these two tasks have significant differences from each other; therefore, the pre-trained model of one task is likely to be difficult to use to predict another task. Sentiment analysis involves determining the overall emotional tone of a text, where the sentence is positive, negative, or neutral. On the contrary, cyberbullying detection involves identifying specific patterns of harmful words. Yet, there are some sentiment analysis approaches that can be used to identify cyberbullying. [PERSON] et al. (2015) proposed an approach for detecting cyberbullying using sentiment analysis techniques. [PERSON] et al. (2016) presented a novel method for identifying online bullying on social media sites from sentiment analysis. [PERSON] et al. (2017) presented a novel framework for supervised learning that uses sentiment analysis to identify cyberbullying. Overall, while sentiment analysis models may be helpful for cyberbullying detection, they cannot be directly reused without significant modifications and additional training. Cyberbullying detection (i.e., yes/no classes) largely needs to identify negative words, which are used to harass a person, while sentiment analysis has three different classes (i.e., negative, positive, and neutral) where negative patterns are part of the problem. In this case, positive and neutral categories are also dominant class labels. Since the nature of the outputs is different in two different problems, we cannot completely reuse one pre-trained model for other cases.