Journal ArticleDOI
Novel OGBEE-based feature selection and feature-level fusion with MLP neural network for social media multimodal sentiment analysis
S. Bairavel,M. Krishnamurthy +1 more
- Vol. 24, Iss: 24, pp 18431-18445
TLDR
The proposed approach investigates the sentiments that are collected from the web recordings that utilize audio, video, and textual modalities for further extraction and utilizes multilayer perceptron-based neural network (MLP-NN) for sentiment classification.Abstract:
Numerous public networks, namely Instagram, YouTube, Facebook, Twitter, etc., share their own feelings and idea as videotapes, posts, and pictures. In future research, adapting to such data and mining valuable information from it will be an undeniably troublesome errand. This paper proposes a novel audio–video–textual-based multimodal sentiment analysis approach. The proposed approach investigates the sentiments that are collected from the web recordings that utilize audio, video, and textual modalities for further extraction. A feature-level fusion technique is employed in fusing the extracted features from different modalities. Therefore, the extracted features are optimally chosen by using a novel oppositional grass bee optimization (OGBEE) algorithm to obtain the best optimal feature set. Here, 12 benchmark functions are developed to validate the numerical efficiency and the effectiveness of a novel OGBEE algorithm for various aspects. Moreover, our proposed approach utilizes multilayer perceptron-based neural network (MLP-NN) for sentiment classification. The experimental analysis reveals that the proposed approach provides better classification accuracy of about 95.2% with less computational time.read more
Citations
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Journal ArticleDOI
Sentiment Classification Algorithm Based on Multi-Modal Social Media Text Information
TL;DR: In this paper, the authors proposed a sentiment classification algorithm based on multi-modal social media text information, which makes use of parallel convolutional neural networks (CNN) and recurrent neural network (RNN) to process text information and user attributes respectively, and combines the feature vectors of the two models for classification, which is called User attributes Convolutional and Recurrent Neural Network (UCRNN).
Journal ArticleDOI
A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN
TL;DR: A comprehensive review of various deep learning algorithms can be found in this paper , which includes multi-layer perception, self-organizing map and deep belief networks algorithms, as well as the various applications of those algorithms in various fields such as wireless networks, ad hoc networks, mobile ad hoc and vehicular adhoc networks, speech recognition engineering, medical applications, natural language processing, material science and remote sensing applications.
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Identifying key success factors for startups With sentiment analysis using text data mining
TL;DR: In this article , a sentiment analysis is done with various predictive models including random forest, support vector machine (SVM) and multilayer perceptron (MLP) to test the labeling of unlabeled data.
Proceedings ArticleDOI
In Your Face: Sentiment Analysis of Metaphor with Facial Expressive Features
TL;DR: Wang et al. as mentioned in this paper presented a novel neural network approach to sentiment analysis of metaphorical expressions that combines both linguistic and visual features and refer to it as the multimodal model approach.
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Mitigating theft-of-service attack - Ensuring cloud security on virtual machines
TL;DR: In this article , the authors proposed a solution to the problem of data security, privacy, faith and a secure cloud infrastructures that could jeopardise cloud computing's widespread acceptance and benefits.
References
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BookDOI
A Practical Guide to Sentiment Analysis
TL;DR: The main aim of this book is to provide a feasible research platform to ambitious researchers towards developing the practical solutions that will be indeed beneficial for the authors' society, business and future researches as well.