Bibliometric Analysis of Mixed Text Using Transformer-Based Architecture in Africa

Abstract

Deep learning techniques based on neural networks have been developed for text creation, a critical sub-task of natural language generation that aims to create human-readable content. Natural language processing (NLP) tasks are utilized to recognize speech in code-mixed comments on social media platforms like Facebook and Twitter, which enable users to interact and exchange ideas, views, status updates, pictures, and videos with people all over the world. Although NLP is widely investigated in the world and Africa is home to approximately 3,000 languages, many of which are derived from significant language families, in this regard, there are challenges that Africa faces in Natural Language Processing (NLP), especially mixed text using transformer-based architecture. The purpose of this study is to investigate the prevalence of mixed text using transformer-based architecture in Africa. Bibliometric analysis was used to assess natural language and mixed text in Africa, utilizing transformer-based architecture. show that sentiment analysis is the holistic tool that is used for mixed text using transformers, where social media, deep learning, codes, computational linguistics, and social networking are critical tools in generating human-like quality text. Therefore, this study proposes artificial intelligence, artificial neutral networks, and neural networks, as well as a prediction to estimate the technique or fluctuation as the method for mixed text using transformer-based architecture in Africa. This research sets the path for future studies that use mixed text using transformer-based architecture in Africa.