An Experimental Analysis of Deep Neural Network Based Classifiers for Sentiment Analysis Task

Shukla, Mrigank; Kumar, Akhil

doi:10.1109/access.2023.3266640

Cited by 4 publications

(1 citation statement)

References 49 publications

(71 reference statements)

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“…The proposed model achieved an accuracy of 87.94%. Shukla and Kumar (2023) proposed a hybrid Transformer-BiLSTM-CNN-based model trained and tested on the SST-2 data set for sentiment analysis. The proposed hybrid model achieved an accuracy of 89.04%.…”

Section: Related Workmentioning

confidence: 99%

A novel multi-layer feature fusion-based BERT-CNN for sentence representation learning and classification

Alyoubi,

Alotaibi,

Kumar

et al. 2023

RIA

Self Cite

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Purpose The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis. Design/methodology/approach The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model. Findings On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets. Originality/value The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.

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Section: Related Workmentioning

confidence: 99%

A novel multi-layer feature fusion-based BERT-CNN for sentence representation learning and classification

Alyoubi,

Alotaibi,

Kumar

et al. 2023

RIA

Self Cite

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FeDN2: Fuzzy-Enhanced Deep Neural Networks for Improvement of Sentence-Level Sentiment Analysis

Phan,

Pham,

Nguyen

2023

Cybernetics and Systems

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Persian Typographical Error Type Detection Using Deep Neural Networks on Algorithmically-Generated Misspellings

Dehghani,

Faili

2023

Preprint

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Spelling correction is a remarkable challenge in the field of natural language processing. The objective of spelling correction tasks is to recognize and rectify spelling errors automatically. The development of applications that can effectually diagnose and correct Persian spelling and grammatical errors has become more important in order to improve the quality of Persian text. The Typographical Error Type Detection in Persian is a relatively understudied area. Therefore, this paper presents a compelling approach for detecting typographical errors in Persian texts. Our work includes the presentation of a publicly available dataset called FarsTypo, which comprises 3.4 million words arranged in chronological order and tagged with their corresponding part-of-speech. These words cover a wide range of topics and linguistic styles. We develop an algorithm designed to apply Persian-specific errors to a scalable portion of these words, resulting in a parallel dataset of correct and incorrect words. By leveraging FarsTypo, we establish a strong foundation and conduct a thorough comparison of various methodologies employing different architectures. Additionally, we introduce a groundbreaking Deep Sequential Neural Network that utilizes both word and character embeddings, along with bidirectional LSTM layers, for token classification aimed at detecting typographical errors across 51 distinct classes. Our approach is contrasted with highly advanced industrial systems that, unlike this study, have been developed using a diverse range of resources. The outcomes of our final method proved to be highly competitive, achieving an accuracy of 97.62%, precision of 98.83%, recall of 98.61%, and surpassing others in terms of speed.

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An Experimental Analysis of Deep Neural Network Based Classifiers for Sentiment Analysis Task

Cited by 4 publications

References 49 publications

A novel multi-layer feature fusion-based BERT-CNN for sentence representation learning and classification

A novel multi-layer feature fusion-based BERT-CNN for sentence representation learning and classification

FeDN2: Fuzzy-Enhanced Deep Neural Networks for Improvement of Sentence-Level Sentiment Analysis

Persian Typographical Error Type Detection Using Deep Neural Networks on Algorithmically-Generated Misspellings

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