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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

BERT-Based Hybrid RNN Model for Multi-class Text Classification to Study the Effect of Pre-trained Word Embeddings

Author 1: Shreyashree S
Author 2: Pramod Sunagar
Author 3: S Rajarajeswari
Author 4: Anita Kanavalli

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130979

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

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Abstract: Due to the Covid-19 pandemic which started in the year 2020, many nations had imposed lockdown to curb the spread of this virus. People have been sharing their experiences and perspectives on social media on the lockdown situation. This has given rise to increased number of tweets or posts on social media. Multi-class text classification, a method of classifying a text into one of the pre-defined categories, is one of the effective ways to analyze such data that is implemented in this paper. A Covid-19 dataset is used in this work consisting of fifteen pre-defined categories. This paper presents a multi-layered hybrid model, LSTM followed by GRU, to integrate the benefits of both the techniques. The advantages of word embeddings techniques like GloVe and BERT have been implemented and found that, for three epochs, the transfer learning based pre-trained BERT-hybrid model performs one percent better than GloVe-hybrid model but the state-of-the-art, fine-tuned BERT-base model outperforms the BERT-hybrid model by three percent, in terms of validation loss. It is expected that, over a larger number of epochs, the hybrid model might outperform the fine-tuned model.

Keywords: Multi-class text classification; transfer learning; pre-training; word embeddings; GloVe; bidirectional encoder representations from transformers; long short-term memory; gated recurrent units; hybrid model; RNN

Shreyashree S, Pramod Sunagar, S Rajarajeswari and Anita Kanavalli, “BERT-Based Hybrid RNN Model for Multi-class Text Classification to Study the Effect of Pre-trained Word Embeddings” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130979

@article{S2022,
title = {BERT-Based Hybrid RNN Model for Multi-class Text Classification to Study the Effect of Pre-trained Word Embeddings},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130979},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130979},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Shreyashree S and Pramod Sunagar and S Rajarajeswari and Anita Kanavalli}
}


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