MULTILINGUAL SENTIMENT ANALYSIS: OVERCOMING CHALLENGES IN CROSS-LANGUAGE SENTIMENT DETECTION WITH NLP

Abstract

This paper explores the complex landscape of sentiment analysis across multiple languages, leveraging advanced natural language processing (NLP) techniques. As digital communication spans a multitude of languages, understanding sentiment in diverse linguistic contexts is increasingly critical. The study emphasizes the importance of multilingual sentiment analysis for businesses, governments, and organizations to engage with global audiences effectively. The research highlights several challenges inherent in multilingual sentiment analysis, including language-specific syntactic and semantic nuances, uneven availability of linguistic resources, and the impact of cultural context on sentiment interpretation. Traditional methods, such as multilingual pre-trained models and translation-based approaches, have shown limitations in accuracy and effectiveness. This paper critiques existing methods and proposes novel solutions to enhance sentiment detection across languages. The methodology integrates state-of-the-art multilingual pre-trained models (e.g., multilingual BERT, XLM-R) with cross-lingual transfer learning, data augmentation, sentiment lexicons, and domain adaptation. By fine-tuning these models on sentiment-labeled datasets and employing cross-lingual embeddings, the approach aims to improve sentiment analysis performance, particularly for under-resourced languages. Data augmentation through machine translation further enriches training datasets, while customized sentiment lexicons and rule-based methods address linguistic and cultural nuances. The results demonstrate significant improvements in sentiment analysis accuracy, with enhanced performance metrics across various languages. The integrated approach effectively mitigates challenges related to linguistic diversity and data scarcity, paving the way for more accurate and contextually relevant sentiment analysis tools. Future research should focus on refining these methodologies and exploring additional languages to advance multilingual sentiment analysis further.