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Enhanced Arabic Sentiment Analysis Using a Novel Stacking Ensemble of Hybrid and Deep Learning Models
2022
Applied Sciences
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Hybrid models based on CNN combined with long short-term memory (LSTM) or gated recurrent unit (GRU) have further improved the performance of single DL models. ...
In this paper, we proposed a stacking ensemble model that combined the prediction power of CNN and hybrid deep learning models to predict Arabic sentiment accurately. ...
The performance of the Arabic sentiment analysis system was examined in relation to the use of ensemble stacking models based on CNN, a hybrid CNN-LSTM model, and a hybrid CNN-GRU model. ...
doi:10.3390/app12188967
fatcat:tlmisywsqzdahdhqz5747bttue
Systematic reviews in sentiment analysis: a tertiary study
2021
Artificial Intelligence Review
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According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis. ...
Different features, algorithms, and datasets used in sentiment analysis models are mapped. ...
study and performance assessment CNN, LSTM, GRU, RNN, ReNN P20 Salur and Aydin (2020) A novel hybrid deep learning model for sentiment classification CNN, LSTM, GRU P21 Aslam et al. (2020) A novel framework ...
doi:10.1007/s10462-021-09973-3
fatcat:zo7igc4fnnh47kyafncbfmaf3u
Sentiment analysis using deep learning approaches: an overview
2019
Science China Information Sciences
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This study also provides the performance analysis of different deep learning models on a particular dataset at the end of each sentiment analysis task. ...
However, it is still a challenge to these traditional approaches to adjust a designed model for a specific task to a new task, especially lexicon based methods. ...
Application DL models for multi-modal sentiment analysis are sorted into four categories: CNN, RNN, RNN with attention and DRL based models. CNN based model. Cambria et al. ...
doi:10.1007/s11432-018-9941-6
fatcat:nbevrfiyybhszirol2af26c6ve
A Novel Hybrid Network for Arabic Sentiment Analysis using fine-tuned AraBERT model
2021
International Journal on Electrical Engineering and Informatics
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in the classification phase, we compared the hybrid model with convolutional neural network (CNN), long short-term memory (LSTM), BiLSTM, and GRU, which are prevalently preferred in sentiment analysis ...
In this context, we finetuned the Arabic BERT (AraBERT) parameters and we used it on three merged datasets to impart its knowledge for the Arabic sentiment analysis. ...
In [5] ; the authors proposed a hybrid model (Bi-LSTM+CNN +additional attention mechanism) that makes use of the advantages of BiLSTM and CNN. ...
doi:10.15676/ijeei.2021.13.4.3
fatcat:rkpdwefewbf7telekofumem7wa
An Improved Model for Analyzing Textual Sentiment Based on a Deep Neural Network Using Multi-Head Attention Mechanism
2021
Applied System Innovation
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short-term memory units (Bi-LSTM) with a convolutional neural network (CNN). ...
To address these issues, we propose a hybrid model that combines a deep neural network with a multi-head attention mechanism (DNN–MHAT). ...
The second step is a high precision based on the lexicon method. A hybrid model for concept-based sentiment analysis combines machine learning methods and lexicon-based proposed by Mudians et al. ...
doi:10.3390/asi4040085
fatcat:chwkaygecjeftiyje66enbfuk4
A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis
[article]
2020
arXiv
pre-print
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Finally, it proposes a novel precisely extreme multi-channel hybrid methodology which outperforms state-of-the-art adapted machine and deep learning approaches by the figure of 9%, and 4% in terms of F1 ...
Roman Urdu Sentiment Analysis, Pretrain word embeddings for Roman Urdu, Word2Vec, Glove, Fast-Text ...
Considering our proposed sentiment analysis methodology is hybrid in nature, we adapted a hybrid model based on CNN, and LSTM [55] presented by Chen et al. ...
arXiv:2003.05443v1
fatcat:zyyqrtodzvfpxflggsl7ton2ea
Extracting Aspect Terms using CRF and Bi-LSTM Models
2020
Procedia Computer Science
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The models proposed for ATE of Hindi Reviews are Conditional Random Field (CRF) and Bidirectional Long-Short-Term-Memory (Bi-LSTM) models with novel architecture. ...
The models proposed for ATE of Hindi Reviews are Conditional Random Field (CRF) and Bidirectional Long-Short-Term-Memory (Bi-LSTM) models with novel architecture. ...
Such dependencies can be better modeled using Bi-LSTM model. In [33] , the Sentiment Analysis task was addressed using 3 CNN layers, max-pooling layer and lastly fully connected layer. ...
doi:10.1016/j.procs.2020.03.301
fatcat:bzy627juivaqdcri44p2c3oa5a
A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis
2020
IEEE Access
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Considering our proposed sentiment analysis methodology is hybrid in nature, we adapted a hybrid model based on CNN and LSTM [63] presented by Chen et al. ...
Finally, we present a novel precisely extreme-multi-channel hybrid methodology for Roman Urdu sentiment analysis. ...
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. ...
doi:10.1109/access.2020.3030885
fatcat:74xtllokurhfjdzpjg2ispgaxq
Sentiment Analysis Using Gated Recurrent Neural Networks
2020
SN Computer Science
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We have implemented the baseline models for LSTM, GRU and Bi-LSTM and Bi-GRU on an Amazon review dataset. ...
Text sentiment analysis is an important and challenging task. Sentiment analysis of customer reviews is a common problem faced by companies. ...
[33] proposed a new model based on the capsule and RNN, i.e., the capsule model, sentiment classification and analysis. ...
doi:10.1007/s42979-020-0076-y
fatcat:xnfzd4xpzzbx3lyg3bppj3jnrm
An Insight on Sentiment Analysis Research from Text using Deep Learning Methods
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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In this paper, we have examined the research works which have used the DL based Sentiment Analysis (SA) for the social network data. ...
The main focus of this paper is to explore how the DL algorithms can enhance the performance of SA than the traditional machine learning algorithms for text based analysis. ...
F1- and LSTM Domain dataset model called NeuroSent for Score: Giulio Sentiment multi-domains was obtained the 87.30 [35] Analysis best results than the baseline model on the Dranziera dataset. 2. ...
doi:10.35940/ijitee.j9316.0881019
fatcat:xa2nlkan3jau7diytj43jczjum
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances
[article]
2022
arXiv
pre-print
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., emotion recognition and sentiment analysis). ...
baseline dataset, fusion strategies for multimodal affective analysis, and unsupervised learning models. ...
For example, Mousa and Schuller [140] designed a novel generative approach, contextual Bi-LSTM with a language model (cBi-LSTM LM), which changes the structure of Bi-LSTM to learn the word's contextual ...
arXiv:2203.06935v3
fatcat:h4t3omkzjvcejn2kpvxns7n2qe
ArabicDialects: An Efficient Framework for Arabic Dialects Opinion Mining on Twitter using Optimized Deep Neural Networks
2021
IEEE Access
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manuscript.Ibrahim AbdEllatif : performed the experiments and analyzed the results and wrote the paper. ...
Fatma Helmy: discussed the results and wrote the paper. Ahmed Elsawy : discussed the results and revised the paper. All authors reads and approved the work in this paper. ...
A hybrid approach between CNN
and LSTM was proposed in [34] and [35]. ...
doi:10.1109/access.2021.3094173
fatcat:3hmw674qt5fp3jk4m46j2lr4z4
Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach
2023
Computer systems science and engineering
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Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory (CNN-LSTM) network. ...
Sentiment analysis of drug reviews has a large potential for providing valuable insights into these cases. ...
[12] propose a deep learning pre-training and multi-task learning model based on double Bi-GRU (Bi-Gated Recurrent Unit). ...
doi:10.32604/csse.2023.029059
fatcat:zdimducgszah5ip7fpy5764zly
Arabic Tweets-Based Sentiment Analysis to Investigate the Impact of COVID-19 in KSA: A Deep Learning Approach
2023
Big Data and Cognitive Computing
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As a result, this study aims at Arabic tweet-based sentiment analysis considering the COVID-19 pandemic in Saudi Arabia. ...
Convolutional neural networks (CNN) and bi-directional long short memory (BiLSTM) deep learning algorithms were applied for classifying the sentiment of Arabic tweets. ...
Researchers in [25] applied a deep neural network model consisting of a combination of a bi-directional LSTM algorithm and a CNN algorithm for Arabic dialectal sentiment analysis. ...
doi:10.3390/bdcc7010016
fatcat:66n33eraevfwtmchun6otdyywa
COVID-19 Tweets Classification Based on a Hybrid Word Embedding Method
2022
Big Data and Cognitive Computing
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The novelty of this study is based on hybrid features extraction, where we combined syntactic features (TF-IDF) with semantic features (FastText and Glove) to represent posts accurately, which helps in ...
SVM outperformed the other models by 88.72%, as well as for XGBoost, with an 85.29% accuracy score. ...
Acknowledgments: The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4360867DSR01). ...
doi:10.3390/bdcc6020058
fatcat:e2wgpvmtorfthbdtlefrirrole
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