## 2 Toxic Comments Classification  
Sentiment analysis in general considered a research area which combines NLP and text mining to automatically detect and identify the opinions contained in the text and determine the writer'sopinion or attitude with regards to a particular topic [10]. Although multiple approaches have been applied in this field, most of them are based on the application of machine learning methods. A specific sub-section of sentiment analysis is a detection of abusive language in the conversational content. Use of aggressive or offensive language in online discussions may occur in various forms. Various studies address different aspects of the abusive language in the online discussions, often differentiated by the types of aggression expressed. Therefore, when considering the abusive language detection from the texts, various related tasks are explored, including detection of cyber-bullying, hate or hate speech, online harassment, flaming, toxic comments, extremism, etc. Those tasks are often not clearly distinguishable, often are overlapping, and despite the differences between the concepts, often similar methods are utilized to tackle those problems [11]. However, there are studies trying to establish the common typology of the different abusive language detection tasks [12]. Toxic comments detection can be considered as a specific sub-task of approaches mentioned above, which aims to detect and identify the toxicity in the conversational text. It is usually solved as a text classification task, where the input features are extracted from the piece of text. As multiple types of toxicity could be contained in the text (e.g., insults, obscene language, hate, etc. ), therefore toxic comments detection is usually considered as a multi-class classification task where the target class describe the particular type of the toxicity contained in the text. In this case, the problem of unbalanced data is a common issue, as the frequency of occurrence of the different toxicity types may vary. Recently, the essential source of the data used to build the toxicity detection models come from social networks. Data are usually extracted from the discussions, comments or social network posts and typically represent the user reactions to a particular topic [13]. During recent years, several datasets became publicly available, containing labelled data from different social platforms and areas, e.g., Twitter dataset [14] contains 25,000 manually annotated tweets containing hate speech. Youtube dataset [15] consists of 3221 manually labelled comments from YouTube discussions [16] or very popular Wikipedia talk page corpus also used in this work. However, different datasets are often labelled non-consistently, which could be the effect of the different problem understanding and will require a more integrated approach when collecting the data in the future [17]. To detect the toxicity in the conversational data, both traditional machine learning methods, as well as advanced deep learning techniques, have been utilized. Traditional machine learning approaches include the use of various classifiers, e.g., Decision Trees [18], Logistic Regression [19], Support Vector Machine models [15] or Ensemble Models [20]. Traditional machine learning models are frequently used and popular in the detection of other types of anti-social behaviour, such as fake reviews detection. For example, work Naive Bayes and Random Forests have been used in the detection of the fake reviews obtained from Amazon [21] using data describing the seller, website, product, reviewer and review content. Authors in [22] answered interesting questions, if the performance of the classification methods for fake reviews filtering are affected when they are used in real-world scenarios that require online learning. Regarding the toxicity detection, authors in [23] monitored and analyzed the most recently published comments to detect whether an aggressive action emerges in a discussion thread.