Using Twitter to collect a multi-dialectal corpus of Albanian using advanced geotagging and dialect modeling.

Abstract

In this study, we present the acquisition and categorization of a geographically-informed, multi-dialectal Albanian National Corpus, derived from Twitter data. The primary dialects from three distinct regions-Albania, Kosovo, and North Macedonia-are considered. The assembled publicly available dataset encompasses anonymized user information, user-generated tweets, auxiliary tweet-related data, and annotations corresponding to dialect categories. Utilizing a highly automated scraping approach, we initially identified over 1,000 Twitter users with discernible locations who actively employ at least one of the targeted Albanian dialects. Subsequent data extraction phases yielded an augmentation of the preliminary dataset with an additional 1,500 Twitterers. The study also explores the application of advanced geotagging techniques to expedite corpus generation. Alongside experimentation with diverse classification methodologies, comprehensive feature engineering and feature selection investigations were conducted. A subjective assessment is conducted using human annotators, which demonstrates that humans achieve significantly lower accuracy rates in comparison to machine learning (ML) models. Our findings indicate that machine learning algorithms are proficient in accurately differentiating various Albanian dialects, even when analyzing individual tweets. A meticulous evaluation of the most salient attributes of top-performing algorithms provides insights into the decision-making mechanisms utilized by these models. Remarkably, our investigation revealed numerous dialectal patterns that, despite being familiar to human annotators, have not been widely acknowledged within the broader scientific community.