Knowledge-based Data Processing for Multilingual Natural Language Analysis

Natural Language Processing (NLP) aids the empowerment of intelligent machines by enhancing human language understanding for linguistic-based human-computer communication. Recent developments in processing power, as well availability large volumes linguistic data, have enhanced demand data-driven methods automatic semantic analysis. This paper proposes multilingual data using feature extraction with classification deep learning architectures. Here, input text has been collected based on various languages and processed to remove missing values null values. The extracted Histogram Equalization Global Local Entropy (HEGLE) classified Kernel-based Radial basis Function (Ker_Rad_BF). These architectures could be utilized process natural language. We present solutions sentiment analysis issue this research article implementing algorithms, we compare precision factors discover optimum option For HASOC dataset, proposed HEGLE_ Ker_Rad_BF achieved an accuracy 98%, a 97%, recall 90.5%, f-1 score 85%, RMSE 55.6% loss curve attained 44%. TRAC is Recall 91%, F-1 87%, neural network 55%.