Department of Information Science, Haramaya University, Dire Dawa, Ethiopia.
School of Information Science, Addis Ababa University, Addis Ababa, Ethiopia.
Sci Rep. 2023 Oct 20;13(1):17982. doi: 10.1038/s41598-023-45137-9.
This study delves into the realm of sentiment analysis in the Amharic language, focusing on political sentences extracted from social media platforms in Ethiopia. The research employs deep learning techniques, including Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN with Bi-LSTM to analyze and classify sentiments. The hybrid CNN-Bi-LSTM model emerges as the top performer, achieving an impressive accuracy of 91.60%. While these results mark a significant milestone, challenges persist, such as the need for a more extensive and diverse dataset and the identification of nuanced sentiments like sarcasm and figurative speech. The study underscores the importance of transitioning from binary sentiment analysis to a multi-class classification approach, enabling a finer-grained understanding of sentiments. Moreover, the establishment of a standardized corpus for Amharic sentiment analysis emerges as a critical endeavor with broad applicability beyond politics, spanning domains like agriculture, industry, tourism, sports, entertainment, and satisfaction analysis. The exploration of sarcastic comments in the Amharic language stands out as a promising avenue for future research.
本研究深入探讨阿姆哈拉语情感分析领域，专注于从埃塞俄比亚社交媒体平台提取的政治语句。研究采用深度学习技术，包括卷积神经网络（CNN）、双向长短时记忆网络（Bi-LSTM）以及结合 CNN 和 Bi-LSTM 的混合模型，对情感进行分析和分类。混合 CNN-Bi-LSTM 模型表现出色，准确率达到令人印象深刻的 91.60%。虽然这些结果标志着一个重要的里程碑，但仍存在挑战，例如需要更广泛和多样化的数据集，以及识别讽刺和比喻等细微情感。研究强调了从二元情感分析向多类分类方法转变的重要性，从而能够更精细地理解情感。此外，建立阿姆哈拉语情感分析的标准化语料库成为一项关键任务，其应用不仅限于政治领域，还涵盖农业、工业、旅游、体育、娱乐和满意度分析等领域。探索阿姆哈拉语中的讽刺评论是未来研究的一个有前途的方向。
