Alexander Naumov
Papers
1
Total Citations
16
H-Index
1
About
Alexander Naumov is a leading researcher in computational linguistics and natural language processing, with a primary focus on emotion detection and sentiment analysis in Russian-language texts. His most influential work, the 2021 paper "Data-Driven Model for Emotion Detection in Russian Texts," has garnered 16 citations and introduced a novel approach to emotion recognition that leverages ELMo language model embeddings to capture nuanced emotional cues in Russian text data. This contribution addresses a critical gap in multilingual NLP, as most emotion detection systems are optimized for English. Naumov's methodology combines deep learning with linguistic features specific to Russian morphology and syntax, achieving state-of-the-art performance in classifying emotions such as joy, sadness, anger, and fear. His research has practical applications in social media monitoring, customer feedback analysis, and mental health support systems. By advancing data-driven techniques for under-resourced languages, Naumov has established himself as a key figure in Slavic NLP, demonstrating how sophisticated vector representations can unlock emotional intelligence in non-English text corpora.
Research Focus
Key Achievements
Top Papers
- 1Data-Driven Model for Emotion Detection in Russian Texts16 citations · 2021