Deep Learning Model for Fine-Grained Aspect-Based Opinion Mining

Abas, Ahmed R.; El-Henawy, Ibrahim M.; Mohamed, Hossam H.; AbdelLatif, Amr M.

doi:10.1109/access.2020.3008824

Cited by 33 publications

(7 citation statements)

References 36 publications

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“…Tang and T discussed the integration and integration modes of online education resources and put forward three integration modes, namely, the education resource management database mode, the education resource center mode, and the distributed education resource network mode. ere are three aspects of educational resource sharing in the network environment: self-sharing, sharing with others, and global educational resource sharing [8]. Aydn used a k-means clustering algorithm to cluster online public opinions as topics, so as to monitor public opinions [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Tao

2022

Mathematical Problems in Engineering

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The key sentimental words in the text cannot be paid attention to effectively, and language knowledge such as the text information and the sentimental resources are relied on. Therefore, it is necessary to make full use of this unique sentimental information to achieve the best performance of the model. In order to solve the problems, a method based on the fusion of the convolutional neural network and the bidirectional GRU network text sentiment analysis capsule model to analyze the ideological and political education of public opinion is put forward. In this model, each sentiment category is combined with the attention mechanism to generate feature vectors to construct sentiment capsules. Finally, the text sentiment categories are judged according to the attributes of the capsules. The model is tested on MR, IMDB, SST-5, and the data set of the ideological and political education review. Experimental results show that compared with MC-CNN-LSTM, the readiness rate of the proposed model is improved by 5.1%, 2.8%, 2.8%, and 1.6% on four public Chinese and English data sets, respectively. Compared with LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, the accuracy of the proposed MC-BiGRU-Capsule model on MR and SST-5 data are 3.2%, 2.4%, and 3.4% higher than that of the LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, respectively. It also shows a better classification effect on multiclassification data sets. It is concluded that compared with other baseline models, this method has a better classification effect.

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Section: Literature Reviewmentioning

confidence: 99%

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Tao

2022

Mathematical Problems in Engineering

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“…Li et al [43] also proposed the BERT-CRF model for End-to-End ABSA. In [44], they presented the FAGOM model for aspect level opinion mining; they used the BERT model for context embedding and a multihead attention mechanism. Finally, a pooling layer is added to extract local and global features.…”

Section: A English and Other Languagesmentioning

confidence: 99%

Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

2022

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The exponential growth of the internet and a multi-fold increase in social media users in the last decade have resulted in a massive growth of unstructured data. Aspect-Based Sentiment Analysis (ABSA) is challenging because it performs a fine-grain analysis; it is a text analysis technique where the opinions group is based on the aspect. The Aspect Extraction (AE) task is one of the core subtasks of ABSA; it helps to identify aspect terms in the text, comments, or reviews. The challenge of the Arabic AE task increases due to the complexity of the Arabic language. This work aims to develop the Arabic AE task by proposing transfer learning using State-of-art pre-trained contextual language models. We concatenate the Bidirectional Encoder Representation from Transformers (BERT) language model and contextualize string embedding (Flair embedding) as a stacked embeddings layer for better word representation for Arabic language. Then, we extend it with different deep learning network architectures. For Arabic AE, the model is developed by concatenating the Arabic contextual language model, AraBERT, and Flair embedding as a contextual stacked embeddings layer with an extended layer, BiLSTM-CRF or BiGRU-CRF, for sequence labeling. Our proposed models are called BF-BiLSTM-CRF and BF-BiGRU-CRF. The proposed model is evaluated using the Arabic Hotel's reviews dataset. For performance evaluation, we used the F1 score. The experimental results show that the proposed BF-BiLSTM-CRF configuration outperformed the baseline and other models by achieving an F1score of 79.7%.

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“…After the Convolutional layer, the Max-pooling layer minimizes and down-samples the features in the feature map. A max-pooling operation is applied over each function map [47]. The idea is to select the highest value for each vector dimension to capture the most significant function.…”

Section: Pooling Layermentioning

confidence: 99%

BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

Abas¹,

El-Henawy²,

Zidan³

et al. 2022

Computers, Materials &Amp; Continua

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Due to the widespread usage of social media in our recent daily lifestyles, sentiment analysis becomes an important field in pattern recognition and Natural Language Processing (NLP). In this field, users' feedback data on a specific issue are evaluated and analyzed. Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research. Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature. Emotions describe a state of mind of distinct behaviors, feelings, thoughts and experiences. The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text. This model is formed by a combination of the Bidirectional Encoder Representations from Transformer (BERT) and the Convolutional Neural networks (CNN) for textual classification. This model embraces the BERT to train the word semantic representation language model. According to the word context, the semantic vector is dynamically generated and then placed into the CNN to predict the output. Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets. The BERT-CNN model achieves an accuracy of 94.7% and an F1-score of 94% for semeval2019 task3 dataset and an accuracy of 75.8% and an F1-score of 76% for ISEAR dataset.

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Deep Learning Model for Fine-Grained Aspect-Based Opinion Mining

Cited by 33 publications

References 36 publications

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

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