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📄 Abstract
Abstract: Proverbs are among the most fascinating linguistic phenomena that transcend
cultural and linguistic boundaries. Yet, much of the global landscape of
proverbs remains underexplored, as many cultures preserve their traditional
wisdom within their own communities due to the oral tradition of the
phenomenon. Taking advantage of the current advances in Natural Language
Processing (NLP), we focus on Greek proverbs, analyzing their sentiment.
Departing from an annotated dataset of Greek proverbs, we expand it to include
local dialects, effectively mapping the annotated sentiment. We present (1) a
way to exploit LLMs in order to perform sentiment classification of proverbs,
(2) a map of Greece that provides an overview of the distribution of sentiment,
(3) a combinatory analysis in terms of the geographic position, dialect, and
topic of proverbs. Our findings show that LLMs can provide us with an accurate
enough picture of the sentiment of proverbs, especially when approached as a
non-conventional sentiment polarity task. Moreover, in most areas of Greece
negative sentiment is more prevalent.
Authors (2)
Katerina Korre
John Pavlopoulos
Submitted
October 15, 2025
Key Contributions
This paper explores the sentiment of Greek proverbs using LLMs, creating an annotated dataset that includes local dialects and mapping the sentiment distribution across Greece. It demonstrates how LLMs can be exploited for accurate sentiment classification of proverbs and provides a combinatory analysis of sentiment based on geography, dialect, and topic, offering insights into cultural wisdom.
Business Value
This research contributes to the digital humanities and cultural preservation by leveraging AI to analyze and understand traditional wisdom. It can inform educational tools, cultural heritage projects, and linguistic research.