AMLBDNLP 2022 : Special Issue in mathematical modeling in engineering .. IF=1.5 Advanced Machine Learning for Big Data Analytics in Natural Language Processing

Machine learning has proven effective, robust, and efficient in solving clustering, forecasting, classification, association rules, etc. The emergence of unexpected very large-scale data being generated at very high velocity from various sources such as the web, sensors, social media, mobile phones, etc. mostly in natural language have led the world to the era of big data. The data being generated is characterized by variety – that is the data in different forms: structured, semi-structured, and unstructured. Such kind of data set can be effectively handled by the flexibility characteristic of the advanced machine learning methodologies because it gives the algorithms the capability to effectively handle different types of data – text, audio, video, and images. Big data poses a new challenge to machine learning algorithms. Despite the progress recorded by advanced machine learning algorithms such as deep learning architectures in the big data analytics for natural language, the progress is still in its early stage beseeching for novel machine learning-based analytical methodologies. Big data analytics require novel new analytical approaches to process the big data mostly in natural language to make it valuable for informing decision making.



This special issue aimed to address the challenges of advanced machine learning approaches within the context of big data analytics for natural language processing and serve as a platform for dissemination as well as sharing of the latest scientific contributions from machine learning methodologies for big data in natural language processing.



The special issue welcomes submissions of high-quality original works, review/survey articles, extended papers with at least 70% new materials, and theories that described significant scientific contributions on the aspect of advanced machine learning for big data analytics in natural language processing on the following topics of interest but not limited to Cyberbullying, multilingual, fake news, emotions, hate speech, sarcasm, stylometry, etc.