During the pandemic, when people needed to physically distance, social media platforms have been one of the outlets where people expressed their opinions, thoughts, sentiments, and emotions regarding the pandemic situation. The core object of this research study is the sentiment analysis of peoples’ opinions expressed on Facebook regarding the current pandemic situation in low-resource languages. To do this, we have created a large-scale dataset comprising of 10,742 manually classified comments in the Albanian language. Furthermore, in this paper we report our efforts on the design and development of a sentiment analyser that relies on deep learning. As a result, we report the experimental findings obtained from our proposed sentiment analyser using various classifier models with static and contextualized word embeddings, that is, fastText and BERT, trained and validated on our collected and curated dataset. Specifically, the findings reveal that combining the BiLSTM with an attention mechanism achieved the highest performance on our sentiment analysis task, with an F1 score of 72.09%.

中文翻译：

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深度学习情绪分析器，用于使用低资源语言的社交媒体评论

在大流行期间，当人们需要拉开距离时，社交媒体平台已成为人们表达有关大流行情况的观点，思想，情感和情感的渠道之一。这项研究的核心目标是对人们在Facebook上表达的关于当前资源匮乏的大流行情况的观点进行情感分析。为此，我们创建了一个大型数据集，其中包含10,742条阿尔巴尼亚语手动分类注释。此外，在本文中，我们报告了我们在基于深度学习的情感分析器的设计和开发方面所做的努力。结果，我们报告了使用各种带有静态和上下文化词嵌入的分类器模型从拟议的情感分析器获得的实验结果，即 fastText和BERT，在我们收集和整理的数据集上经过培训和验证。具体而言，研究结果表明，结合BiLSTM和注意力机制，我们的情绪分析任务获得了最高的表现，F1得分为72.09％。