## 2 Related Work  
Media monitoring has been a crucial area of focus for many years, as it involves tracking and analyzing a wide range of media outlets to glean actionable insights [4]. Efforts to enhance the capabilities of media monitoring systems have been continuous, particularly in the realm of processing and understanding content in diverse languages such as Swahili [5], Hungarian [6], and many others. This multilingual approach is vital as it allows for more comprehensive coverage across different geopolitical and cultural landscapes, enabling businesses and organizations to maintain a global perspective. Today, the market offers a variety of media monitoring solutions, each designed to meet the specific needs of different user groups. For instance, social media monitoring tools have become indispensable in the journalism sector, significantly streamlining the workflow for journalists by providing quick access to trending topics and public opinions [7]. Similarly, businesses leverage these tools to gain deeper insights into customer behavior, market trends, and the effectiveness of marketing campaigns [8]. These insights are crucial for developing targeted strategies that resonate well with the intended audience. However, despite the wide array of tools available, a significant limitation remains. Many of these tools are predominantly tailored for larger languages, which are more commonly spoken and hence provide a broader audience base. This focus on major languages often leaves lesser-spoken languages under-represented, thereby limiting the scope of media monitoring to more general and broadly applicable purposes. Furthermore, the existing tools tend to prioritize general usability over specialized functions, which can dilute the effectiveness of media monitoring in niche or specialized contexts. The comprehensive list of these tools, while extensive (available at [[https://www.stateofdigitalpublishing.com/digital-platform-tools/best-media-monitoring-tools/](https://www.stateofdigitalpublishing.com/digital-platform-tools/best-media-monitoring-tools/)]([https://www.stateofdigitalpublishing.com/digital-platform-tools/best-media-monitoring-tools/](https://www.stateofdigitalpublishing.com/digital-platform-tools/best-media-monitoring-tools/)) (accessed on 10 March 2024)), highlights the need for more nuanced and adaptable solutions that can cater to the unique demands of diverse linguistic and cultural groups. Media monitoring is a multifaceted field involving several key analytical tasks critical for effectively interpreting vast amounts of media data. These tasks include topic classification, which categorizes content into predefined topics; Named Entity Recognition (NER), which identifies and classifies key elements like names, organizations, and locations within text; sentiment analysis, aimed at determining the emotional tone behind a body of text; topic modeling, which identifies the underlying themes within large volumes of text; and event detection, which focuses on identifying significant occurrences reported in the media. Each of these tasks addresses a unique aspect of media analysis, contributing to a comprehensive understanding of the media landscape. Despite the broad spectrum of tasks associated with media monitoring, our research specifically focuses on text classification. This focus is driven by the critical role text classification plays in structuring unstructured data, thus facilitating more effective data analysis and interpretation. The Papers with Code repository ([[https://paperswithcode.com/task/text-classification](https://paperswithcode.com/task/text-classification)]([https://paperswithcode.com/task/text-classification](https://paperswithcode.com/task/text-classification)) (accessed on 10 March 2024)) serves as a valuable resource in this area, listing approximately 1000 scientific articles that explore various aspects of text classification.