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Deep Learning Model for Fine-Grained Aspect-Based Opinion Mining
Faculty
Computer Science
Year:
2020
Type of Publication:
ZU Hosted
Pages:
128845-128855
Authors:
Ahmed Raafat Abass Mohamed Saliem
Staff Zu Site
Abstract In Staff Site
Journal:
IEEE ACCESS IEEE
Volume:
volume 8
Keywords :
Deep Learning Model , Fine-Grained Aspect-Based Opinion
Abstract:
Despite the great manufactures' efforts to achieve customer satisfaction and improve their performance, social media opinion mining is still on the y a big challenge. Current opinion mining requires sophisticated feature engineering and syntactic word embedding without considering semantic interaction between aspect term and opinionated features, which degrade the performance of most of opinion mining tasks, especially those that are designed for smart manufacturing. Research on intelligent aspect level opinion mining (AOM) follows the fast proliferation of user-generated data through social media for industrial manufacturing purposes. Google's pre-trained language model, Bidirectional Encoder Representations from Transformers (BERT) widely overcomes existing methods in eleven natural language processing (NLP) tasks, which makes it the standard way for semantic text representation. In this paper, we introduce a novel deep learning model for ne-grained aspect-based opinion mining, named as FGAOM. First, we train the BERT model on three specic domain corpora for domain adaption, then use adjusted BERT as embedding layer for concurrent extraction of local and global context features. Then, we propose Multi-head Self-Attention (MSHA) to effectively fuse internal semantic text representation and take advantage of convolutional layers to model aspect term interaction with surrounding sentiment features. Finally, the performance of the proposed model is evaluated via extensive experiments on three public datasets. Results show that performance of the proposed model outperforms performances of recent the-of-the-art models.
Author Related Publications
Ahmed Raafat Abass Mohamed Saliem, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021
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Ahmed Raafat Abass Mohamed Saliem, "Using General Regression with Local Tuning for Learning Mixture Models from Incomplete Data Sets", ScienceDirect, 2010
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Ahmed Raafat Abass Mohamed Saliem, "On determining efficient finite mixture models with compact and essential components for clustering data", ScienceDirect, 2013
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Ahmed Raafat Abass Mohamed Saliem, "Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data", ScienceDirect, 2012
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Ahmed Raafat Abass Mohamed Saliem, "Adaptive competitive learning neural networks", ScienceDirect, 2013
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Department Related Publications
Heba Zaki Mohamed Abdallah Elfiqi, "A computational linguistic approach for the identification of translator stylometry using Arabic-English text", IEEE, 2011
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Heba Zaki Mohamed Abdallah Elfiqi, "Measuring Complexity of Mouse Brain Morphological Changes Using GeoEntropy", AIP Publishing, 2009
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Mohammed Abdel Basset Metwally Attia, "Solving systems of nonlinear equations via conjugate direction flower pollination algorithm", inderscience, 2017
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Mustafa Khamis Baz Ramadan, "An Efficient method for choosing most suitable cloud storage provider reducing top security risks based on multi-criteria neutrosophic decision making", An Efficient method for choosing most suitable cloud storage provider reducing top security risks based on multi-criteria neutrosophic decision making, 2017
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