Attentive convolutional gated recurrent network: a contextual model to sentiment analysis

Habimana, Olivier; Li, Yuhua; Li, Ruixuan; Gu, Xiwu; Yan, Weirong

doi:10.1007/s13042-020-01135-1

Cited by 8 publications

(5 citation statements)

References 44 publications

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“…The results outperformed the Stanford Sentiment Treebank dataset with the provided model. Habimana et al (2020) presented a novel technique by considering the contextual features of the sentiment classification using a convolutional gated recurrent network (ACGRN) to prioritize the feature knowledge for improved sentiment detection. The experiments were conducted at six different size real-time datasets and found that the proposed approach outperforms significantly.…”

Section: Related Workmentioning

confidence: 99%

ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis

Kamyab

Rasool

et al. 2022

PeerJ Computer Science

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Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been successfully applied to Natural Language Processing (NLP), especially in sentiment analysis. NLP can execute numerous functions to achieve significant results through RNN and CNN. Likewise, previous research shows that RNN achieved meaningful results than CNN due to extracting long-term dependencies. Meanwhile, CNN has its advantage; it can extract high-level features using its local fixed-size context at the input level. However, integrating these advantages into one network is challenging because of overfitting in training. Another problem with such models is the consideration of all the features equally. To this end, we propose an attention-based sentiment analysis using CNN and two independent bidirectional RNN networks to address the problems mentioned above and improve sentiment knowledge. Firstly, we apply a preprocessor to enhance the data quality by correcting spelling mistakes and removing noisy content. Secondly, our model utilizes CNN with max-pooling to extract contextual features and reduce feature dimensionality. Thirdly, two independent bidirectional RNN, i.e., Long Short-Term Memory and Gated Recurrent Unit are used to capture long-term dependencies. We also applied the attention mechanism to the RNN layer output to emphasize each word’s attention level. Furthermore, Gaussian Noise and Dropout as regularization are applied to avoid the overfitting problem. Finally, we verify the model’s robustness on four standard datasets. Compared with existing improvements on the most recent neural network models, the experiment results show that our model significantly outperformed the state-of-the-art models.

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Section: Related Workmentioning

confidence: 99%

ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis

Kamyab

Rasool

et al. 2022

PeerJ Computer Science

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“…Convolutional neural network (CNN) is one of the representative algorithms of deep learning, which is widely used due to its strong feature extraction ability. It is mainly composed of convolutional layer, pooling layer and fully-connected layer [28][29][30]. The main function of convolutional layer is to perform convolution operation and extract features from input information.…”

Section: Clstm Modelmentioning

confidence: 99%

A multi-task prediction method for acid concentration based on attention-CLSTM

Zhu

Liu

Cao

et al. 2022

Preprint

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The accurate measurement of acid concentration, including hydrogen chloride (HCl) and ferrous chloride concentrations (FeCl2), is a critical part of ensuring the quality of strip steel pickling. In this study, a multi-task attention convolutional long short-term memory (MACL) neural network model was proposed to predict hydrogen ion and ferrous ion concentrations simultaneously. Firstly, in order to extract significant information from the input sequence, an attention mechanism was added to the model to calculate the correlation between the input features and the acid concentration at each time step. Next, multi-task learning explores the connections between the two tasks and exploits hidden constraints to improve prediction accuracy. Finally, deep features were extracted through convolutional long short-term memory (CLSTM) neural network, and the acid concentration was predicted. The proposed MACL model was compared with other popular prediction models. The experimental results show that proposed MACL model generally outperforms other models, indicating that proposed model has excellent predictive performance and effectiveness.

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“…Typically, CNNs have three main types of layer: an input layer, a feature extraction layer and a classification layer. CNN models are extremely effective in feature representation, including object recognition [67], sentiment analysis [14], and question answering [34].…”

Section: Convolutional Neural Network Based Modelsmentioning

confidence: 99%

“…The result is a computer that can understand the content of a document, including the contextual nuances of the language in the document [13]. The main challenges in NLP for QA include speech recognition, sentiment analysis [14], information extraction [15], text summarization [16], natural-language generation [17], etc.…”

Section: Introductionmentioning

confidence: 99%

A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches

Mutabazi

Tang

et al. 2021

Applied Sciences

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The advent of Question Answering Systems (QASs) has been envisaged as a promising solution and an efficient approach for retrieving significant information over the Internet. A considerable amount of research work has focused on open domain QASs based on deep learning techniques due to the availability of data sources. However, the medical domain receives less attention due to the shortage of medical datasets. Although Electronic Health Records (EHRs) are empowering the field of Medical Question-Answering (MQA) by providing medical information to answer user questions, the gap is still large in the medical domain, especially for textual-based sources. Therefore, in this study, the medical textual question-answering systems based on deep learning approaches were reviewed, and recent architectures of MQA systems were thoroughly explored. Furthermore, an in-depth analysis of deep learning approaches used in different MQA system tasks was provided. Finally, the different critical challenges posed by MQA systems were highlighted, and recommendations to effectively address them in forthcoming MQA systems were given out.

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Attentive convolutional gated recurrent network: a contextual model to sentiment analysis

Cited by 8 publications

References 44 publications

ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis

ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis

A multi-task prediction method for acid concentration based on attention-CLSTM

A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches

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