POLITICAL STANCE DETECTION AND CLASSIFICATION ON TIGRIGNA TEXT USING DEEP LEARNING APPROACHES

Abstract

The rise of social media has transformed public discourse, providing platforms for individuals to express their opinions on various topics, particularly political issues. Political stance detection, which identifies an individual's position on specific topics, has become increasingly important for policymakers, researchers, and organizations aiming to navigate complex social landscapes and make informed decisions. Despite its significance, most research in this area has focused on English and other European languages, with limited attention to Amharic and virtually none to Tigrigna, a language spoken by millions in Eritrea and Ethiopia. This gap is particularly critical given the ongoing socio-political challenges, such as unemployment and civil unrest, in Tigrigna speaking communities. This study addresses the lack of research on political stance detection in Tigrigna by analyzing comments from the TPLF Facebook page. Data was collected us in the Face ¬pager tool, and two feature extraction strategies—Bag of Words (BOW) and Skip¬ gram from Word2Vec—were employed to convert textual data into numerical representations suitable for machine learning. Advanced deep learning algorithms, including Gated Recurrent Unit (GRU), Transposed Gated Recurrent Unit (T¬GRU), and Long Short¬ Term Memory (LSTM), were applied to classify political sentiments toward the TPLF party. The results demonstrate that the Transposed GRU model combined with the Skip¬ gram strategy achieved an accuracy of 82% and an F1¬score of 0.8822, representing a significant advancement in political stance classification for low¬ resource languages. These findings highlight the effectiveness of deep learning approaches in analyzing Tigrigna text and provide a foundational methodology for future research. This study addresses a gap in the existing literature by providing a nuanced analysis of the socio political dynamics within Tigrigna¬ speaking communities, which have been largely overlooked in political discourse research. By utilizing advanced techniques in stance detection, this research enhances our understanding of public sentiment and sets a precedent for scholarly inquiry into underrepresented languages. The contributions are threefold: it establishes a foundational dataset specifically tailored to Tigrigna ¬speaking contexts; it employs innovative natural language processing methods, such as transfer learning and alternative word embed dings; and it considers idiomatic expressions and the role of emojis, offering a more granular understanding of public sentiment. Looking ahead, future research should broaden the dataset to encompass a wider array of political topics and explore advanced machine learning techniques, thereby enriching the findings. This research lays the groundwork for subsequent studies and contributes to a more inclusive understanding of political discourse across diverse linguistic landscapes, ultimately fostering greater engagement with marginalized voices in the political arena.