Citation

Anoop, M. and Sutha, K. and Srinivasan, Uma Shankari and Balamurugan, M. and Anitha, V. and Emerson Raja, Joseph (2024) Neumann Stacked Bilateral Deep Learning based Big Sentiment Data Analytics. Journal of Theoretical and Applied Information Technology, 102 (9). pp. 4005-4022. ISSN 1992-8645

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Abstract

Sentiment analysis extracts information from several text sources like, blogs, reviews, news, and so on. The purpose of sentiment analysis on big data is to classify emotions or opinions into variegated sentiments. Conventional deep learning methods have been developed to classify the tweets. However, longer sentiment analysis time was considered. To address the issue, Neumann Mutual Informative and Stacked Bilateral Deep Learning (NMI-SBDL) for sentiment investigation is proposed to research products or services before making a purchase. First, through the tweets obtained from the Sentiment140 dataset, the Knowledge Sentimental Graph is constructed. Second, computationally-efficient dimensionality reduced tweets are generated by the Neumann Mutual Information-based Feature selection algorithm. Finally, the Stacked Bilateral LSTM-based model is utilized for classifying the tweet polarity. With this robust sentiment analysis is made by the Twitter Application Programming Interface (API) with higher accuracy and lesser computation time. Experimental assessment of the proposed NMI-SBDL and existing methods are carried out with different factors using Python libraries. The results of NMI-SBDL provided for improving the sentiment analysis accuracy, precision, recall and lesser time by 13%, 6%, 6%, and 23% than the existing approaches. The paper concludes with accurate and robust sentiment analysis for big data.

Item Type:	Article
Uncontrolled Keywords:	Big Data, Sentiment Analysis, Neumann Mutual Information, Feature Selection, Stacked Bilateral, Long Short-Term Memory
Subjects:	L Education > LB Theory and practice of education > LB1060 Learning
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Nov 2024 02:27
Last Modified:	04 Nov 2024 02:27
URII:	http://shdl.mmu.edu.my/id/eprint/13096

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