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Using a Novel Hybrid Krill Herd and Bat based Recurrent Replica to Estimate the Sentiment Values of Twitter based Political Data

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Title Using a Novel Hybrid Krill Herd and Bat based Recurrent Replica to Estimate the Sentiment Values of Twitter based Political Data
 
Creator Manikyamba, I Lakshmi
Mohan, A Krishna
 
Subject Big data
Multilingual datasets
Opinion mining
Sentiment analysis
Text summarization
 
Description 93-100
Big data is an essential part of the world since it is directly applicable to many functions. Twitter is an essential social
network or big data replicating political information. However, big data sentiment analysis in opinion mining is challenging
for complex information. In this approach, the Twitter-based political datasets are taken as input. Furthermore, the sentiment
analysis of twitter-based political multilingual datasets like Hindi and English is not easy because of the complicated data.
Therefore, this paper introduces a novel Hybrid Krill Herd and Bat-based Recurrent Replica (HKHBRR) to evaluate the
sentiment values of twitter-based political data. Here, the fitness functions of the krill herd and bat optimization model are
initialized in the dense layer to enhance the accuracy, precision, etc., and also reduce the error rate. Initially, Twitter-based
political datasets are taken as input, and these collected datasets are also trained to this proposed approach. Moreover, the
proposed deep learning technique is implemented in the Python framework. Thus, the outcomes of the developed model are
compared with existing techniques and have attained the finest results of 98.68% accuracy and 0.5% error.
 
Date 2023-01-16T10:33:59Z
2023-01-16T10:33:59Z
2023-01
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61201
https://doi.org/10.56042/jsir.v82i1.69943
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.82(01) [January 2023]