## 9 Conclusions  
This research investigates both closed-set and open-set topic classification problems. For the closed-set topic classification problem, we utilized texts collected from the European Media Monitoring webpage, covering 58 languages and 18 topics. Our comparative experiments explored different embeddings, including word embeddings (bert-base-multilingual-cased), semantic sentence embeddings (LaBSE, text-embedding-ada-002), and contextual embeddings (XLM-R), along with various classification (BERT+CNN, LaBSE+FFNN, LaBSE_fine_tuning, XLM-R_fine_tuning), memory-based (LaBSE_LangChain _k1, LaBSE_LangChain_k10_mv, ADA_LangChain_k1, ADA_LangChain_k10_mv), and generative (Davinci_fine_tuning) approaches under both multilingual and crosslingual scenarios (training on English and testing on any of the 58 languages). Our experimental findings demonstrated the superiority of trainable/fine-tunable approaches over memory-based and generative methods. LaBSE+FFNN emerged as the most accurate method, achieving macro-average F1 score values of 0.944 \(\pm\) 0.015 in multilingual experiments and 0.946 \(\pm\) 0.019 in crosslingual experiments. Furthermore, the experimental investigation revealed similar performance in both multilingual and crosslingual scenarios, thereby affirming the robustness of LaBSE in crosslingual mechanisms and eliminating the need for machine translation of texts into English. Additionally, a deeper analysis of the results indicated a correlation between topics that are less covered in the training dataset and their poorer prediction outcomes. The open-set classification experiments were conducted on the multilingual dataset of known classes, supplemented with additional texts harvested from the European Media Monitoring page. These experiments confirmed the superiority of LaBSE vectorization over other feature types that rely on the output of the closed-set model or the distribution of known classes in the semantic vector space. The average loss of distinguishing _known_ topics from _new_ ones is very low (\(\sim\)0.0017 \(\pm\) 0.0002), and the accuracy of detecting separate emerging specific topics is generally high, with most topics achieving over 0.9. The exception, a lower accuracy value of 0.796 for one broader topic, underscores the challenges of handling topics that have diverse and sometimes overlapping content with others. The open-set and closed-set classification models have been integrated into our media monitoring system, which has been further enhanced with additional clustering modules used to define the boundaries and names of newly detected classes. This system is currently deployed and undergoing testing in real-world scenarios. In the future, we plan to expand the number of topics that are investigated and consider other suggestions from our clients. Conceptualization, J.K.-D.; methodology, J.K.-D.; software, J.K.-D.; validation, J.K.-D.; formal analysis, J.K.-D.; investigation, J.K.-D.; resources, A.U. ; data curation, A.U. ; writing--original draft preparation, J.K.-D. and A.U. ; writing--review and editing, J.K.-D.; visualization, A.U. ; supervision, J.K.-D.; project administration, A.U. ; funding acquisition, A.U. All authors have read and agreed to the published version of the manuscript. The project \"Development of the National Information Impact Identification and Analysis Ecosystem (NAAS)\" (No. 01.2.1-LVPA-V-835-03-000) was financed from the funds of the European Regional Fund under the priority 1 \"Promotion of scientific research, experimental development, and innovation\" measure \"Pre-purchase LT\". Not applicable. Data Availability Statement: The dataset that was created and used for this research is publicly available at [[https://github.com/novian-pro/EMM_18x58_dataset](https://github.com/novian-pro/EMM_18x58_dataset)]([https://github.com/novian-pro/EMM_18x58_dataset](https://github.com/novian-pro/EMM_18x58_dataset)), accessed on 16 May 2024.