  This research conducts a comparative study on multilingual text
classification methods, utilizing deep learning and embedding visualization.
The study employs LangDetect, LangId, FastText, and Sentence Transformer on a
dataset encompassing 17 languages. It explores dimensionality's impact on
clustering, revealing FastText's clearer clustering in 2D visualization due to
its extensive multilingual corpus training. Notably, the FastText multi-layer
perceptron model achieved remarkable accuracy, precision, recall, and F1 score,
outperforming the Sentence Transformer model. The study underscores the
effectiveness of these techniques in multilingual text classification,
emphasizing the importance of large multilingual corpora for training
embeddings. It lays the groundwork for future research and assists
practitioners in developing language detection and classification systems.
Additionally, it includes the comparison of multi-layer perceptron, LSTM, and
Convolution models for classification.
