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<p>This paper presents a hybrid sentiment analysis framework tailored for the Greek language, combining a novel lexicon construction methodology with an Active Learning (AL) strategy to enhance performance and minimize annotation effort. A domain-specific lexicon was developed using two original metrics—Score<sub>w</sub>, capturing the sentiment polarity of words, and Weight<sub>w</sub>, reflecting their combined emotional and statistical importance. These metrics enabled the transformation of tweet-level annotations into semantically rich word-level representations. To evaluate the framework, four classification models (Naïve Bayes, Logistic Regression, SVM, and Decision Trees) were trained with and without lexicon-derived features. The results showed substantial improvements in accuracy and F1-score when incorporating the lexicon, especially for Logistic Regression and Decision Tree models. Furthermore, a dynamic Active Learning approach was employed, where the lexicon was reconstructed in each cycle using the evolving labeled dataset. This strategy achieved the same levels of accuracy and Macro F1-score, as with full-supervision framework, while using only 30% of the data. The findings demonstrate the efficacy of combining lexicon enhancement with uncertainty-based sampling, offering a cost-effective and scalable solution for sentiment analysis in low-resource languages such as Greek.</p>

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Hybrid sentiment analysis approach with domain-specific lexicons with active learning on Greek social media texts

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月5日

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  • Kyriakos Skoularikis,
  • Ilias Savvas,
  • Georgia Garani,
  • George Kakarontzas

摘要

This paper presents a hybrid sentiment analysis framework tailored for the Greek language, combining a novel lexicon construction methodology with an Active Learning (AL) strategy to enhance performance and minimize annotation effort. A domain-specific lexicon was developed using two original metrics—Scorew, capturing the sentiment polarity of words, and Weightw, reflecting their combined emotional and statistical importance. These metrics enabled the transformation of tweet-level annotations into semantically rich word-level representations. To evaluate the framework, four classification models (Naïve Bayes, Logistic Regression, SVM, and Decision Trees) were trained with and without lexicon-derived features. The results showed substantial improvements in accuracy and F1-score when incorporating the lexicon, especially for Logistic Regression and Decision Tree models. Furthermore, a dynamic Active Learning approach was employed, where the lexicon was reconstructed in each cycle using the evolving labeled dataset. This strategy achieved the same levels of accuracy and Macro F1-score, as with full-supervision framework, while using only 30% of the data. The findings demonstrate the efficacy of combining lexicon enhancement with uncertainty-based sampling, offering a cost-effective and scalable solution for sentiment analysis in low-resource languages such as Greek.