Spectral Analysis, Agglomerative, Mean Shift and Affinity Propagation Algorithms, Use on the Content from Social Media for Low-Resource Languages.

Social networks, as part of our daily life, affect our behavior and lifestyle in various ways, making it important for each of us to be aware of their impact. Posts on social media platforms can have a profound effect on our mood, depending on our personal interpretation and opinion of them. Therefore, it is crucial to correctly classify these textual data to gain a better understanding of their impact. However, this task can be challenging, particularly when dealing with unlabeled data such as social media posts. An added challenge is working with low-resource languages. In this research, we investigate four unsupervised text clustering methods by testing them on a low-resource language, such as Albanian. The investigated algorithms are Spectral, Agglomerative, Mean Shift and Affinity Propagation, and by adjusting the working parameters, we tried to find a more appropriate application of them. Methods are applied to pre-processed data, textual posts, by use of different preprocessing techniques, and the results are presented and interpreted. This research aims to assist other researchers in the same field who have a specific focus on working with low-resource languages.