The exponential growth of social media has spurred an increase in the propagation of hate nowadays. Recent evidence shows that hate speech on social media is detrimental to the mental and physical health of individuals. Thus, there is an emerging need for automated hate speech detection. Automated hate speech detection rests on the intersection between Natural Language Processing (NLP) techniques and machine learning models. An introduction of NLP and its utilities, as well as commonly employed features and classification methods in hate speech detection, are discussed. Hate speech detection in non-English languages is needed to tackle this emergent issue in countries where multiple languages are used. Hence, an overview of the current literature on hate speech detection in non-English languages are covered too. Challenges in the field of hate speech detection are explored and the importance of standardized methodologies for building corpora and data sets are emphasized.

Big data has revolutionized every field of life, which accumulates human learning as well. The field of education has progressed in past couple of decades, and addition to that, rapid growth in the number of educational institutions has created a tough competition. The massive accumulation of data in the educational sector has created a great scope of EDM (Educational Data Mining) with the support of robust predictive models. It is quite necessary to regularly examine the performance of the students to make them perform better, thus helps to maintain the reputation of the institution. This study proposed a predictive model through which the performance of the student can be forecasted depending upon various characteristics. The KDD(Knowledge Discovery in Databases) methodology was followed stepwise in this study for developing predictive models to predict student performance. The data balancing techniques such as SMOTE (Synthetic Minority Over-sampling Technique) and ADASYN (Adaptive Synthetic Sampling) were employed to handle the unbalanced effect of data which causes bias predictions. Also, for the selection of significant features techniques, FCBF (Fast Correlation Based Feature selection) and RFE (Recursive Feature Elimination) were used. The EDM algorithms Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized for predicting student performance with suitable hyper-parameter tuning using random search to enhance the performance of the model. The results obtained were cross-validated using Ensemble Method and benchmarked with previous studies. The random forest model achieved the highest accuracy of 86% after data balancing and careful selection of significant features.

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Recommender models for personalized marketing empower businesses to provide personalized recommendations of goods or services to customers to fulfil their requirements, thus ultimately improves the customer buying experience. Various recommender models powered by robust machine learning algorithms were reviewed on the methods and techniques to appraise its performance concerning the personalized marketing campaigns. Recommender models can be broadly categorized into four types such as content-based, collaborative-based, knowledge-based and hybrid-based. The content-based recommendation is suitable when the system, user or product is new where classification and regression algorithms are mostly implemented. The collaborative-based recommendation is suitable when a more accurate prediction is required where Neighbour-based models, Bayesian methods, rule-based models, decision trees, and latent matrix factorization models may be implemented in this scenario. Knowledge-based recommenders are well suited for recommendations that address explicitly defined user requirements. Different types of recommenders use different sources of data and inherently have different strengths and weaknesses. Selecting the suitable recommender model with the consideration of the scenario and domain of application is very crucial. Therefore, an in-depth research is required and done on the emphasis of the application of recommender models in the personalized marketing especially on the hybrid models with a more efficient deployment for mass applications in this contemporary data-driven business world.