Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification

Over the past few decades, text classification problems have been widely utilized in many real time applications. Leveraging the text classification methods by means of developing new applications in the field of text mining and Natural Language Processing (NLP) is very important. In order to accura...

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Autores principales:	Sunil Kumar Prabhakar, Harikumar Rajaguru, Dong-Ok Won
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/313d4bbfdd00409fbc3353e7d5674417
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spelling	oai:doaj.org-article:313d4bbfdd00409fbc3353e7d56744172021-11-08T02:36:46ZPerformance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification1875-919X10.1155/2021/2420254https://doaj.org/article/313d4bbfdd00409fbc3353e7d56744172021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2420254https://doaj.org/toc/1875-919XOver the past few decades, text classification problems have been widely utilized in many real time applications. Leveraging the text classification methods by means of developing new applications in the field of text mining and Natural Language Processing (NLP) is very important. In order to accurately classify tasks in many applications, a deeper insight into deep learning methods is required as there is an exponential growth in the number of complex documents. The success of any deep learning algorithm depends on its capacity to understand the nonlinear relationships of the complex models within data. Thus, a huge challenge for researchers lies in the development of suitable techniques, architectures, and models for text classification. In this paper, hybrid deep learning models, with an emphasis on positioning of attention mechanism analysis, are considered and analyzed well for text classification. The first hybrid model proposed is called convolutional Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism and output (CBAO) model, and the second hybrid model is called convolutional attention mechanism with Bi-LSTM and output (CABO) model. In the first hybrid model, the attention mechanism is placed after the Bi-LSTM, and then the output Softmax layer is constructed. In the second hybrid model, the attention mechanism is placed after convolutional layer and followed by Bi-LSTM and the output Softmax layer. The proposed hybrid models are tested on three datasets, and the results show that when the proposed CBAO model is implemented for IMDB dataset, a high classification accuracy of 92.72% is obtained and when the proposed CABO model is implemented on the same dataset, a high classification accuracy of 90.51% is obtained.Sunil Kumar PrabhakarHarikumar RajaguruDong-Ok WonHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Sunil Kumar Prabhakar Harikumar Rajaguru Dong-Ok Won Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification
description	Over the past few decades, text classification problems have been widely utilized in many real time applications. Leveraging the text classification methods by means of developing new applications in the field of text mining and Natural Language Processing (NLP) is very important. In order to accurately classify tasks in many applications, a deeper insight into deep learning methods is required as there is an exponential growth in the number of complex documents. The success of any deep learning algorithm depends on its capacity to understand the nonlinear relationships of the complex models within data. Thus, a huge challenge for researchers lies in the development of suitable techniques, architectures, and models for text classification. In this paper, hybrid deep learning models, with an emphasis on positioning of attention mechanism analysis, are considered and analyzed well for text classification. The first hybrid model proposed is called convolutional Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism and output (CBAO) model, and the second hybrid model is called convolutional attention mechanism with Bi-LSTM and output (CABO) model. In the first hybrid model, the attention mechanism is placed after the Bi-LSTM, and then the output Softmax layer is constructed. In the second hybrid model, the attention mechanism is placed after convolutional layer and followed by Bi-LSTM and the output Softmax layer. The proposed hybrid models are tested on three datasets, and the results show that when the proposed CBAO model is implemented for IMDB dataset, a high classification accuracy of 92.72% is obtained and when the proposed CABO model is implemented on the same dataset, a high classification accuracy of 90.51% is obtained.
format	article
author	Sunil Kumar Prabhakar Harikumar Rajaguru Dong-Ok Won
author_facet	Sunil Kumar Prabhakar Harikumar Rajaguru Dong-Ok Won
author_sort	Sunil Kumar Prabhakar
title	Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification
title_short	Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification
title_full	Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification
title_fullStr	Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification
title_full_unstemmed	Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification
title_sort	performance analysis of hybrid deep learning models with attention mechanism positioning and focal loss for text classification
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/313d4bbfdd00409fbc3353e7d5674417
work_keys_str_mv	AT sunilkumarprabhakar performanceanalysisofhybriddeeplearningmodelswithattentionmechanismpositioningandfocallossfortextclassification AT harikumarrajaguru performanceanalysisofhybriddeeplearningmodelswithattentionmechanismpositioningandfocallossfortextclassification AT dongokwon performanceanalysisofhybriddeeplearningmodelswithattentionmechanismpositioningandfocallossfortextclassification
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Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification

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