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An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding
The automated identification of toxicity in texts is a crucial area in text analysis since the social media world is replete with unfiltered content that ranges from mildly abusive to downright hateful. Researchers have found an unintended bias and unfairness caused by training datasets, which cause...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863472/ https://www.ncbi.nlm.nih.gov/pubmed/35211168 http://dx.doi.org/10.1155/2022/8467349 |
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author | Alsharef, Ahmad Aggarwal, Karan Sonia, Koundal, Deepika Alyami, Hashem Ameyed, Darine |
author_facet | Alsharef, Ahmad Aggarwal, Karan Sonia, Koundal, Deepika Alyami, Hashem Ameyed, Darine |
author_sort | Alsharef, Ahmad |
collection | PubMed |
description | The automated identification of toxicity in texts is a crucial area in text analysis since the social media world is replete with unfiltered content that ranges from mildly abusive to downright hateful. Researchers have found an unintended bias and unfairness caused by training datasets, which caused an inaccurate classification of toxic words in context. In this paper, several approaches for locating toxicity in texts are assessed and presented aiming to enhance the overall quality of text classification. General unsupervised methods were used depending on the state-of-art models and external embeddings to improve the accuracy while relieving bias and enhancing F1-score. Suggested approaches used a combination of long short-term memory (LSTM) deep learning model with Glove word embeddings and LSTM with word embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT), respectively. These models were trained and tested on large secondary qualitative data containing a large number of comments classified as toxic or not. Results found that acceptable accuracy of 94% and an F1-score of 0.89 were achieved using LSTM with BERT word embeddings in the binary classification of comments (toxic and nontoxic). A combination of LSTM and BERT performed better than both LSTM unaccompanied and LSTM with Glove word embedding. This paper tries to solve the problem of classifying comments with high accuracy by pertaining models with larger corpora of text (high-quality word embedding) rather than the training data solely. |
format | Online Article Text |
id | pubmed-8863472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-88634722022-02-23 An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding Alsharef, Ahmad Aggarwal, Karan Sonia, Koundal, Deepika Alyami, Hashem Ameyed, Darine Comput Intell Neurosci Research Article The automated identification of toxicity in texts is a crucial area in text analysis since the social media world is replete with unfiltered content that ranges from mildly abusive to downright hateful. Researchers have found an unintended bias and unfairness caused by training datasets, which caused an inaccurate classification of toxic words in context. In this paper, several approaches for locating toxicity in texts are assessed and presented aiming to enhance the overall quality of text classification. General unsupervised methods were used depending on the state-of-art models and external embeddings to improve the accuracy while relieving bias and enhancing F1-score. Suggested approaches used a combination of long short-term memory (LSTM) deep learning model with Glove word embeddings and LSTM with word embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT), respectively. These models were trained and tested on large secondary qualitative data containing a large number of comments classified as toxic or not. Results found that acceptable accuracy of 94% and an F1-score of 0.89 were achieved using LSTM with BERT word embeddings in the binary classification of comments (toxic and nontoxic). A combination of LSTM and BERT performed better than both LSTM unaccompanied and LSTM with Glove word embedding. This paper tries to solve the problem of classifying comments with high accuracy by pertaining models with larger corpora of text (high-quality word embedding) rather than the training data solely. Hindawi 2022-02-15 /pmc/articles/PMC8863472/ /pubmed/35211168 http://dx.doi.org/10.1155/2022/8467349 Text en Copyright © 2022 Ahmad Alsharef et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Alsharef, Ahmad Aggarwal, Karan Sonia, Koundal, Deepika Alyami, Hashem Ameyed, Darine An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding |
title | An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding |
title_full | An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding |
title_fullStr | An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding |
title_full_unstemmed | An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding |
title_short | An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding |
title_sort | automated toxicity classification on social media using lstm and word embedding |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863472/ https://www.ncbi.nlm.nih.gov/pubmed/35211168 http://dx.doi.org/10.1155/2022/8467349 |
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