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Assessment and recommendation of neural networks and precise techniques for sentiment systems analysis

Assessment and recommendation of neural networks and precise techniques for sentiment systems... Sentiment analysis (SA) is a critical research issue in the realm of emotion. Artificial intelligence (AI) recognizes the polarity of an opinion, sentiments, and the amount of sadness communicated within user's social media positions, with subjectivity of documents or digital texts. This work presents a thorough assessment and review of current approaches and algorithms for semantic analysis. The critical contribution of this work is that it gives the most up-to-date picture of research work done in SA and recent trends in the field. It presents a profound classification of these techniques. It also focuses on challenges and emerging research areas in SA. The researchers have identified various types of emotions from user's inputs. For classifying the user feelings, they have effectively employed advanced and updated deep learning classifiers/models such as long-short-term-memory (LSTM), recurrent neural network (RNN), and convolutional neural network (CNN), capsule network (CapsNet) in their works. The glove2 pre-trained normalized phrase indices is primarily used in distinguishing emotion types. Various authors have used hyper parameter tuning to avoid overfitting and readying a better model for SA. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Ambient Intelligence and Humanized Computing Springer Journals

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References (39)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1868-5137
eISSN
1868-5145
DOI
10.1007/s12652-023-04643-4
Publisher site
See Article on Publisher Site

Abstract

Sentiment analysis (SA) is a critical research issue in the realm of emotion. Artificial intelligence (AI) recognizes the polarity of an opinion, sentiments, and the amount of sadness communicated within user's social media positions, with subjectivity of documents or digital texts. This work presents a thorough assessment and review of current approaches and algorithms for semantic analysis. The critical contribution of this work is that it gives the most up-to-date picture of research work done in SA and recent trends in the field. It presents a profound classification of these techniques. It also focuses on challenges and emerging research areas in SA. The researchers have identified various types of emotions from user's inputs. For classifying the user feelings, they have effectively employed advanced and updated deep learning classifiers/models such as long-short-term-memory (LSTM), recurrent neural network (RNN), and convolutional neural network (CNN), capsule network (CapsNet) in their works. The glove2 pre-trained normalized phrase indices is primarily used in distinguishing emotion types. Various authors have used hyper parameter tuning to avoid overfitting and readying a better model for SA.

Journal

Journal of Ambient Intelligence and Humanized ComputingSpringer Journals

Published: Aug 1, 2023

Keywords: Capsule network; Long-short-term memory; Deep neural networks; Recurrent neural network; Natural language processing; Word embeddings; Sentiment analysis

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