Multilingual Sentiment Analysis using Deep Learning Approach

This paper explores sentiment analysis in Natural Language Processing (NLP) with a focus on employing BERT (Bidirectional Encoder Representations from Transformers) models.Sentiment analysis involves identifying the emotional tone in textual content and categorizing it as positive, negative, or neutral, with applications in various domains like social media monitoring and customer feedback analysis employing BERT for multilingual sentiment analysis showcases remarkable potential in capturing nuanced sentiments across diverse languages.Its contextual understanding and pre-trained language representations enable effective sentiment classification, transcending linguistic boundaries.By leveraging BERT, we enhance the accuracy and adaptability of sentiment analysis models, facilitating cross-cultural understanding and market insights.