A novel, gradient boosting framework for sentiment analysis in languages where NLP resources are not plentiful: a case study for modern Greek

DOI10.3390/A10010034zbMath1461.62219OpenAlexW2594108779WikidataQ114854804 ScholiaQ114854804MaRDI QIDQ1662634

Vasileios Athanasiou, Manolis Maragoudakis

Publication date: 20 August 2018

Published in: Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/a10010034

zbMATH Keywords

high-dimensional datasentiment analysisgradient boosting machinesmodern Greek

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics (62P99) Natural language processing (68T50) Linguistics (91F20)

Related Items (2)

A novel, gradient boosting framework for sentiment analysis in languages where NLP resources are not plentiful: a case study for modern Greek ⋮ The role of entanglement for enhancing the efficiency of quantum kernels towards classification

Uses Software

• Sentilo
• SMOTE

Cites Work

• Unnamed Item
• Feature generation using general constructor functions
• A novel, gradient boosting framework for sentiment analysis in languages where NLP resources are not plentiful: a case study for modern Greek
• Learning Deep Architectures for AI

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