2020
DOI: 10.1109/access.2020.2992063
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Multi-View Deep Network: A Deep Model Based on Learning Features From Heterogeneous Neural Networks for Sentiment Analysis

Abstract: By the development of social media, sentiment analysis has changed to one of the most remarkable research topics in the field of natural language processing which tries to dig information from textual data containing users' opinions or attitudes toward a particular topic. In this regard, deep neural networks have emerged as promising techniques that have been extensively used for this aim in recent years and obtained significant results. Considering the fact that deep neural networks can automatically extract … Show more

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Cited by 70 publications
(23 citation statements)
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“…Bensalem et al [63] laid out an edge server based on a deep neural network and proposed the best layout model. Different kinds of deep neural networks have the ability to extract various features from data because they have their own distinct structures, which can help with model optimization [64] . In the process of designing the optimal layout model, the authors [63] put forward the formula of deep neural network parameter selection after comprehensively considering the communication delay between nodes and the cost of EC nodes.…”
Section: Ai For Edge Server Placementmentioning
confidence: 99%
“…Bensalem et al [63] laid out an edge server based on a deep neural network and proposed the best layout model. Different kinds of deep neural networks have the ability to extract various features from data because they have their own distinct structures, which can help with model optimization [64] . In the process of designing the optimal layout model, the authors [63] put forward the formula of deep neural network parameter selection after comprehensively considering the communication delay between nodes and the cost of EC nodes.…”
Section: Ai For Edge Server Placementmentioning
confidence: 99%
“…The empirical result demonstrates that this approach achieves a better accuracy, which equals 99.97 % and 99.83 % on the COVID-19_Sentiments and the Sentiment 140 datasets. In [69], Sadr et al developed a new multi-view deep learning classifier based on convolutional and recursive neural networks for classifying the text sentiment. The experimental results proved that the suggested multi-view deep learning classifier surpasses the single-view deep learning classifier in terms of accuracy.…”
Section: Previous Researchmentioning
confidence: 99%
“…A new version of the standard Optimized Link State Routing (OLSR) protocol for SGs to improve the management of control intervals that enhance the efficiency of the standard OLSR protocol [27] without affecting its reliability. Machine learning approaches are the most significant for sentiment analysis, which are logistic regression, decision tree, naive Bayes, Support Vector Machine (SVM), etc., have attained better results [19,29]. However, different hand-crafted features need manual design and adjustment that are cost-intensive and time-consuming.…”
Section: Introductionmentioning
confidence: 99%