Improving sentiment analysis of Ukrainian-language content by applying a rule-based approach

Sentiment analysis is a fundamental component of natural language processing (NLP), enabling the automated assessment of textual sentiment across different languages.However, widely used sentiment analysis tools, such as VADER, often struggle with language-specific challenges, particularly in morphologically rich and syntactically complex languages like Ukrainian.This study introduces an improved rule-based sentiment analysis algorithm specifically designed for Ukrainian-language texts, addressing the limitations of generic approaches.The proposed algorithm integrates an enhanced lexicon, including the EMOLEX sentiment dictionary, polarity scores, emoji sentiment mapping, and intensity boosters, to refine sentiment classification.Additionally, advanced dependency parsing and position-aware scoring mechanisms are employed to improve contextual understanding, enabling more accurate differentiation between positive, negative, and neutral sentiments.These enhancements are particularly crucial for capturing Ukrainian-specific linguistic structures, which pose difficulties for existing sentiment analysis models.The algorithm's effectiveness was evaluated using Ukrainian-language datasets, comparing its performance against the widely used VADER sentiment analysis tool.The results demonstrate that the custom algorithm significantly outperforms VADER in detecting sentiment polarity, particularly in cases with strong positive or negative sentiment.This confirms the necessity of languagespecific sentiment analysis tools for non-English content, as they provide greater accuracy and contextual sensitivity.Despite the promising results, further improvements remain possible.One key area for future research involves integrating artificial intelligence (AI) techniques, such as machine learning and deep learning, to create a hybrid framework that enhances the accuracy of sentiment classification, especially for ambiguous or nuanced expressions.