A NOVEL SECURITY FRAMEWORK FOR PREVENTING ATTACKS IN SENTIMENT ANALYSIS

Nowadays, Twitter data-based sentiment analysis is the mainly common topic in Natural Language Processing (NLP). Nevertheless, security attacks on Twitter data are increased day by day because hackers and the attacks will reduce the performance of sentiment analysis. Many kinds of research are developed to overcome this problem, but there are no accurate results found. So this current research proposed a novel Ant Lion honeypot with Regression (ALHR) for detecting the attacks and continuous monitoring of data. Moreover, the fitness function of the introduced replica is used for preventing attacks and continuous monitoring. Also, this model utilizes Twitter-based data about the corona disease 2019 (COVID-19) for detecting attacks and enhances the classification of sentiments using continuous monitoring. For verifying the effectiveness of ALHR technique, launch attacks in classification layer. The developed technique is executed in Python, and the achieved performance metrics are compared with another existing replica regarding the accuracy, recall, precision, F-measure, and error rate. Finally, the ALHR technique enhances the sentiment analysis and provides continuous monitoring. © 2022, Engg Journals Publications. All rights reserved.

DETECTING MALICIOUS ACTIVITIES on TWITTER DATA for SENTIMENT ANALYSIS USING A NOVEL OPTIMIZED MACHINE LEARNING APPROACH

In Natural Language Processing (NLP), Twitter data is used for sentiment analysis and it is most prevalent theme in recent era. However, the security attacks on the Twitter data have been increased by hackers which reduced the performance of the sentiment analysis. Thus to detect the malicious activities in the Twitter data, a novel Spider Monkey based Generalized Intelligent (SMbGI) framework is developed in this paper. This model utilizes Twitter-based data about the coronavirus disease 2019 (COVID-19) to detect the malware activities for improving the classification of sentiments. Moreover, this model imposed a malicious attack on the data for recognizing the developed SMbGI model efficiencies. Thus, the proposed SMbGI approach has been effectually detecting malicious functions and enhances sentiment classification. Moreover, Python tool is used for sentiment analysis, and it computed the parameters like accuracy, recall, precision, F-measure, and error rate. Lastly, the attained outcomes are compared with recent existing works to identify the performance of the SMbGI approach. © 2021 Little Lion Scientific