Linguistic Based Emotion Detection from Live Social Media Data Classification Using Metaheuristic Deep Learning Techniques

Abstract

A crucial area of research that can reveal numerous useful insights is emotional recognition. Several visible ways, including speech, gestures, written material, and facial expressions, can be used to portray emotion. Natural language processing (NLP) and DL concepts are utilised in the content-based categorization problem that is at the core of emotion recognition in text documents.This research propose novel technique in linguistic based emotion detection by social media using metaheuristic deep learning architectures. Here the input has been collected as live social media data and processed for noise removal, smoothening and dimensionality reduction. Processed data has been extracted and classified using metaheuristic swarm regressive adversarial kernel component analysis. Experimental analysis has been carried out in terms of precision, accuracy, recall, F-1 score, RMSE and MAP for various social media dataset.

Research on artificial intelligence machine translation based on BP neural algorithm

“…Currently, the field of artificial intelligence faces many challenges in the development of machine translation, including issues related to poor accuracy [4], significant discrepancies in translation content, and incomplete translation methodologies. Hence, several researchers advocate for the use of intelligent algorithms in the field of machine translation, with the aim of identifying mistake sources and enhancing the process of selecting the most optimal translation technique [5]. Intelligent algorithms are used to analyse machine translation and develop coherent translation schemes in order to enhance the efficacy of machine translation [6].…”