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

Kamyab, Marjan; Li, Guohua; Rasool, Abdur; Adjeisah, Michael

doi:10.7717/peerj-cs.877

Cited by 22 publications

(8 citation statements)

References 59 publications

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“…In traditional neural networks, all the input variables are independent of the output variable. Some of the NLP problem examples, such as predicting if the sentence is positive or negative, spam classifier or time-series data, stock forecasting or sales forecasting, can be solved by RNN [ 49 ]. Bag of words, term frequency-inverse document frequency and Word2VEC are used for text preprocessing which convert text into vectors to solve NLP problems in machine learning.…”

Section: Methodsmentioning

confidence: 99%

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

Adak

Pradhan

Shukla

et al. 2022

Foods

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The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs such as Uber Eats, Menulog, and Deliveroo, customer reviews on internet platforms have become a valuable source of information about a company’s performance. FDS organisations strive to collect customer complaints and effectively utilise the information to identify improvements needed to enhance customer satisfaction. However, only a few customer opinions are addressed because of the large amount of customer feedback data and lack of customer service consultants. Organisations can use artificial intelligence (AI) instead of relying on customer service experts and find solutions on their own to save money as opposed to reading each review. Based on the literature, deep learning (DL) methods have shown remarkable results in obtaining better accuracy when working with large datasets in other domains, but lack explainability in their model. Rapid research on explainable AI (XAI) to explain predictions made by opaque models looks promising but remains to be explored in the FDS domain. This study conducted a sentiment analysis by comparing simple and hybrid DL techniques (LSTM, Bi-LSTM, Bi-GRU-LSTM-CNN) in the FDS domain and explained the predictions using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The DL models were trained and tested on the customer review dataset extracted from the ProductReview website. Results showed that the LSTM, Bi-LSTM and Bi-GRU-LSTM-CNN models achieved an accuracy of 96.07%, 95.85% and 96.33%, respectively. The model should exhibit fewer false negatives because FDS organisations aim to identify and address each and every customer complaint. The LSTM model was chosen over the other two DL models, Bi-LSTM and Bi-GRU-LSTM-CNN, due to its lower rate of false negatives. XAI techniques, such as SHAP and LIME, revealed the feature contribution of the words used towards positive and negative sentiments, which were used to validate the model.

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Section: Methodsmentioning

confidence: 99%

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

Adak

Pradhan

Shukla

et al. 2022

Foods

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“…The third gate, the control gate, decides which values will be updated (either 0 or 1), for which a tanh layer creates a vector of Ĉt . The last gate, the output gate, determines the value of the next hidden state [54].…”

Section: Phase IImentioning

confidence: 99%

AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

Aslam,

Manzoor

et al. 2024

Symmetry

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The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. The significance of this work lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model’s exceptional performance, achieving a notable 96.04% accuracy compared to existing studies. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.

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“…A number of studies have focused on developing new methods for reducing the computational complexity of CNNs. For example, Kamyab et al [ 28 ] proposed an attention-based deep model for sentiment analysis that utilizes a two-channel CNN and a Bi-RNN model (ACR-SA). This model incorporates attention-based mechanisms with novel data processing techniques, word representation, and deep learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

Embedding channel pruning within the CNN architecture design using a bi-level evolutionary approach

Louati

Bechikh

et al. 2023

J Supercomput

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Remarkable advancements have been achieved in machine learning and computer vision through the utilization of deep neural networks. Among the most advantageous of these networks is the convolutional neural network (CNN). It has been used in pattern recognition, medical diagnosis, and signal processing, among other things. Actually, for these networks, the challenge of choosing hyperparameters is of utmost importance. The reason behind this is that as the number of layers rises, the search space grows exponentially. In addition, all known classical and evolutionary pruning algorithms require a trained or built architecture as input. During the design phase, none of them consider the process of pruning. In order to assess the effectiveness and efficiency of any architecture created, pruning of channels must be carried out before transmitting the dataset and computing classification errors. For instance, following pruning, an architecture of medium quality in terms of classification may transform into an architecture that is both highly light and accurate, and vice versa. There exist countless potential scenarios that could occur, which prompted us to develop a bi-level optimization approach for the entire process. The upper level involves generating the architecture while the lower level optimizes channel pruning. Evolutionary algorithms (EAs) have proven effective in bi-level optimization, leading us to adopt the co-evolutionary migration-based algorithm as a search engine for our bi-level architectural optimization problem in this research. Our proposed method, CNN-D-P (bi-level CNN design and pruning), was tested on the widely used image classification benchmark datasets, CIFAR-10, CIFAR-100 and ImageNet. Our suggested technique is validated by means of a set of comparison tests with regard to relevant state-of-the-art architectures.

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

Cited by 22 publications

References 59 publications

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

Embedding channel pruning within the CNN architecture design using a bi-level evolutionary approach

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