An attention-based CNN-LSTM model for subjectivity detection in opinion-mining

Sagnika, Santwana; Mishra, Bhabani Shankar Prasad; Meher, Saroj K.

doi:10.1007/s00521-021-06328-5

Cited by 31 publications

(10 citation statements)

References 31 publications

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“…To show the fault checking results and evaluate the model performance, two metrics, Fault Diagnosis Rate (FDR) and False Positive Rate (FPR), were used to evaluate the model performance. FDR and FPR are defined in [21].…”

Section: Evaluation Resultsmentioning

confidence: 99%

The significance of the convolutional deep learning model in the intelligent collaborative correction of English writing

Wang¹

2023

3C Tecnología

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To ensure the normal operation of English composition grammar correction and avoid inaccurate detection caused by faults, it is of great significance to detect abnormal working conditions in time and diagnose them accurately. Aiming at the complexity of grammar correction, this paper proposes a PLSTM-CNN model for fault detection in the grammar correction process. The model effectively combines the global feature extraction ability of LSTM for time series data and the ability of the CNN model to extract local features, which reduces the loss of feature information and achieves a higher fault detection rate. A one-dimensional dense CNN is used as the main body of the CNN, and the LSTM network is sensitive to changes in sequence information to avoid model overfitting while building a deeper network. The maximum mutual information coefficient (MMIC) data preprocessing method is adopted to improve the local correlation of the data and improve the efficiency of the PLSTM-CNN model to detect faults from different initial conditions. The research results show that the parallel PLSTM-CNN has better prediction performance than the serial PLSTM-CNN, and its FDR and FPR are 90.5% and 0.051, respectively. It shows that the use of convolutional deep learning models for the prediction of writing grammar correction faults has strong application prospects.

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Section: Evaluation Resultsmentioning

confidence: 99%

The significance of the convolutional deep learning model in the intelligent collaborative correction of English writing

Wang¹

2023

3C Tecnología

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“…These applications typically involve a significant number of labels, and the number of labels continues to increase with new applications. Therefore, describing samples with only a single label is challenging [38]. In this study, we address the problem of dual-label classification in the context of scientometric classification of literature, where labels for research content and method must be assigned to each document with high descriptive accuracy.…”

Section: Model Developmentmentioning

confidence: 99%

“…The result is down-sampled or pooled feature maps that highlight the most present feature in the field, rather than the average presence of the feature as in the case of average pooling. In practice, this method has been shown to perform better than average pooling in automated feature extraction, such as feature extraction in text mining applications [37,38].…”

Section: Deep Learning For Literature Classificationmentioning

confidence: 99%

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

et al. 2022

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With the increasing development of published literature, classification methods based on bibliometric information and traditional machine learning approaches encounter performance challenges related to overly coarse classifications and low accuracy. This study presents a deep learning approach for scientometric analysis and classification of scientific literature based on convolutional neural networks (CNN). Three dimensions, namely publication features, author features, and content features, were divided into explicit and implicit features to form a set of scientometric terms through explicit feature extraction and implicit feature mapping. The weighted scientometric term vectors are fitted into a CNN model to achieve dual-label classification of literature based on research content and methods. The effectiveness of the proposed model is demonstrated using an application example from the data science and analytics literature. The empirical results show that the scientometric classification model proposed in this study performs better than comparable machine learning classification methods in terms of precision, recognition, and F1-score. It also exhibits higher accuracy than deep learning classification based solely on explicit and dominant features. This study provides a methodological guide for fine-grained classification of scientific literature and a thorough investigation of its practice.

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“…In this section, we present the details of our deep neural network for citation context classification. We proposed a CNN [53,54] model by employing fastText pre-trained embedding vectors. To handle the data imbalance, we employed using two different stateof-the-art techniques such as class-weight and customized focal loss function to improve the model accuracy.…”

Section: Deep-learning-based Citation Classification Approachmentioning

confidence: 99%

A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

2021

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We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.

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An attention-based CNN-LSTM model for subjectivity detection in opinion-mining

Cited by 31 publications

References 31 publications

The significance of the convolutional deep learning model in the intelligent collaborative correction of English writing

The significance of the convolutional deep learning model in the intelligent collaborative correction of English writing

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

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