基于年龄的LSTM和BERT新闻分类

2022 International Conference on Computational Modelling, Simulation and Optimization (ICCMSO) Pub Date : 2022-12-01 DOI:10.1109/ICCMSO58359.2022.00014

Tahiya Ahmed Chowdhury, Nusrat Jahan Tonoya, Takia Maliha, Parsia Akter, Md. Shahriar Mahbub

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引用次数: 0

摘要

新闻在我们的日常生活中扮演着重要的角色，它教育、告知、提醒甚至娱乐人们。在我们的研究中，我们的目标是将新闻文章分类为成人新闻和儿童新闻，以提供适合年龄的新闻。新闻分类是成人新闻还是儿童新闻，是通过新闻文章分析来完成的。为了实现我们的目标，我们开发了我们的新闻数据集并构建了两个不同的模型:一个使用长短期记忆(LSTM)，另一个使用来自变形金刚的双向编码器表示(BERT)。LSTM模型达到了94.84%的准确率，BERT模型达到了95.68%的准确率，这比LSTM模型在新闻分类上的准确率要高一些。

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Age Based News Classification using LSTM and BERT

News has been playing a major role in our day-to-day life in educating, informing, alerting, and even entertaining people. In our research, we aim on classifying news articles as adult news and kid news with a view to providing age-appropriate news. News classification as adult news or kid news is done by news article analysis. For fulfilling our target, we developed our news dataset and constructed two different models: one using Long short-term memory(LSTM) and the other using Bidirectional Encoder Representations from Transformers(BERT). The LSTM model achieved an accuracy of 94.84% and the BERT model managed to reach an accuracy of 95.68%, which is a bit higher than the LSTM model for news classification.

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2022 International Conference on Computational Modelling, Simulation and Optimization (ICCMSO)

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