Multilingual news feed analysis and Classification strategies using deep Linguistic neural networks

Abstract

Data analysis is the process of gathering the data on specific newlinedomain and extracting the useful information to conclude significant outcomes. newlineData is the information collected from various resources such as web servers and newlinereal time materials. The collected data can be in the format of text files, audio newlinefiles, video files or any multimedia files. Data analysis models and the research newlinefocused on this field vary from one application to another. In this situation, the newlinerecent researches are focusing on the sentiment data analysis models and text newlinemining models for deeply extracting the useful information from the daily data newlinegatherings. newlineThe techniques such as sentiment analysis and text mining are newlinewidely applied in the social networks for analyzing the frequently transferred newlinedata streams. Particularly, the streams of data are evaluated with the help of newlinevarious user reviews, opinions, ratings and emoji appearances. This reveals the newlinesentiments of the multiple users on the particular web feed or any incident that is newlineupdated. In the stream of sentiment analysis, text mining techniques play a major newlinerole with the advanced approaches like Natural Language Processing (NLP), newlineMachine Learning (ML) and Deep Learning (DL) to improve the data newlineclassification accuracy rate. newlineIn this research work, these enhanced techniques are implemented newlineto build an intelligent news feed analysis model. Notably, this research work newlinedevelops different multilingual news feed data analysis techniques with ML and newlineDL based tuning models. Most of the latest research works concentrated on newlinefinding the effectiveness in the monolingual news feed analysis models. newlineHowever, the need for multilingual data analysis system is mandatory for the newlinecountries like India newline