Multilingual News Feed Analysis using Intelligent Linguistic Particle Filtering Techniques
Authors: 
Rakesh Kumar S, Gayathri Nagasubramanian, Muthuramalingam S, Fadi Al-TurjmanAuthors Info & Claims
Rakesh Kumar S, Gayathri Nagasubramanian, Muthuramalingam S, Fadi Al-TurjmanAuthors Info & ClaimsArticle No.: 87, Pages 1 - 19
Abstract
Analyzing real-time news feeds and their impacts in the real world is a complex task in the social networking arena. Particularly, countries with a multilingual environment have various patterns and perceptions of news reports considering the diversity of the people. Multilingual and multimodal news analysis is an emerging trend for evaluating news source neutralities. Therefore, in this work, four new deep news particle filtering techniques were developed, including generic news analysis, sequential importance re-sampling (SIR)-based news particle filtering analysis, reinforcement learning (RL)-based multimodal news analysis, and deep Convolution neural network (DCNN)-based multi-news filtering approach, for news classification. Results indicate that these techniques, which primarily employ particle filtering with multilevel sampling strategies, produce 15% to 20% better performance than conventional news analysis techniques.
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March 2023
570 pages
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Publication History
Published: 10 March 2023
Online AM: 18 November 2022
Accepted: 11 October 2022
Revised: 14 August 2022
Received: 25 February 2022
Published in TALLIP Volume 22, Issue 3
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