A Deep Learning-based Classification Model for Arabic News Tweets Using Bidirectional Long Short-Term Memory Networks

Pub Date : 2024-07-16 DOI:10.47836/pjst.32.4.09

Chin-Teng Lin, Mohammed Thanoon, Sami Karali

引用次数: 0

Abstract

This research develops a classification model for Arabic news tweets using Bidirectional Long Short-Term Memory networks (BiLSTM). Tweets about Arabic news were gathered between August 2016 and August 2020 and divided into five categories. Custom Python scripts, Twitter API and the GetOldTweets3 Python library were used to collect the data. BiLSTM was used to train and test the model. The results indicated an average accuracy, precision, recall, and f1-score of 0.88, 0.92, 0.88, and 0.89, respectively. The results could have practical implications for Arabic machine learning and NLP tasks in research and practice.

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利用双向长短期记忆网络为阿拉伯语新闻推文建立基于深度学习的分类模型

本研究利用双向长短期记忆网络（BiLSTM）开发了一个阿拉伯语新闻推文分类模型。有关阿拉伯语新闻的推文收集于 2016 年 8 月至 2020 年 8 月期间，分为五类。数据收集使用了自定义 Python 脚本、Twitter API 和 GetOldTweets3 Python 库。BiLSTM 用于训练和测试模型。结果显示，平均准确率、精确率、召回率和 f1 分数分别为 0.88、0.92、0.88 和 0.89。这些结果对阿拉伯语机器学习和 NLP 任务的研究和实践具有实际意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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