## 2 Related Work
These articles span 150 benchmarks and involve 132 different datasets, covering a wide range of related tasks including, but not limited to, topic classification, sentiment analysis, intent detection, and hate speech detection. A unifying factor across these diverse text classification tasks is their reliance on supervised machine learning techniques, which require large annotated datasets for model training. The advent of transformer-based models has marked a significant advancement in the field. Techniques such as XLNet [9] and RoBERTa [10] represent some of the most effective approaches currently available. These models have consistently performed at the top of various leaderboards, demonstrating their ability to handle complex, nuanced tasks in text classification. Their architecture, which allows for a deep understanding of context and the relationships between words in longer texts, makes them particularly well-suited for the intricate demands of media monitoring tasks. This high level of performance underscores the importance of sophisticated computational models in tackling the challenges of modern media analysis. Topic classification stands out as one of the most advanced areas within text classification, particularly for languages that are well-supported with substantial resources and research. This maturity has facilitated the development of zero-shot learning models, which have significantly changed the landscape by eliminating the need for traditional supervised data collection or extensive model training. These models can understand and categorize content they have never seen during training, a revolutionary step for scaling applications across languages without annotated data. However, the evolution of zero-shot learning has not just been about broadening its application. Recent shifts in focus have aimed at refining these models to enhance their versatility and accuracy. Techniques such as unsupervised clustering for data compression before classification [11] help reduce the dimensionality of data, making the classification process more efficient without supervised labels. Additionally, fine-tuning models by predicting the first sentence in a paragraph has shown promise in adapting zero-shot models to new tasks and contexts more effectively [12]. These methods improve the model's ability to generalize from limited information and apply its learning to broader scenarios, a crucial enhancement for practical applications. Despite these advancements, zero-shot learning models face significant challenges when applied to less-supported languages. For example, despite its strengths, the XLM-R-based zero-shot model struggles with languages with minimal digital resources, as demonstrated with 10 American low-resourced languages [13]. This limitation highlights a critical gap in the model's ability to deal with linguistic diversity. Additionally, strategies like translating text from low-resourced languages into high-resourced ones before applying zero-shot models can be problematic. This approach, although sometimes effective, may not always capture the nuanced meanings and cultural contexts embedded in the original language, as shown in studies [14]. In our research, we also recognize potential limitations with the application of zero-shot models, particularly in the multilingual and open-set classification scenarios. A pivotal aspect of contemporary text classification is the use of multilingual models, which are equipped to understand and process linguistic nuances across multiple languages. These models are designed to capture the similarities and semantic relationships of words in diverse languages, making them invaluable for global applications. For effective deployment in specific scenarios, these models often undergo a process of fine-tuning to tailor their capabilities to the particular nuances of downstream tasks.