5 papers
Multilingual Sentiment Aware Text Summarization A Reinforcement Learning Approach for Consistency Maintenance
Mikhail Krasitskii, Alexander Gelbukh, Olga Kolesnikova +1
Reinforcement Learning from Human Feedback (RLHF) has significantly improved the quality and fluency of large language models in text summarization. However, its impact on affectiv…
Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis
Mikhail Krasitskii, Grigori Sidorov, Olga Kolesnikova +2
We propose a hybrid approach for multilingual sentiment analysis that combines extractive and abstractive summarization to address the limitations of standalone methods. The model…
Multilingual Sentiment Analysis of Summarized Texts: A Cross-Language Study of Text Shortening Effects
Mikhail Krasitskii, Grigori Sidorov, Olga Kolesnikova +2
Summarization significantly impacts sentiment analysis across languages with diverse morphologies. This study examines extractive and abstractive summarization effects on sentiment…
Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions
Mikhail Krasitskii, Olga Kolesnikova, Liliana Chanona Hernandez +2
The sentiment analysis task in Tamil-English code-mixed texts has been explored using advanced transformer-based models. Challenges from grammatical inconsistencies, orthographic v…
Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages
Mikhail Krasitskii, Olga Kolesnikova, Liliana Chanona Hernandez +2
This study explores transformer-based models such as BERT, mBERT, and XLM-R for multi-lingual sentiment analysis across diverse linguistic structures. Key contributions include the…