Published Paper Details:

Cross-Lingual Sentiment Analysis (CLSA) has emerged as a vital instrument for comprehending public sentiment around the world as digital communication crosses language and geographic barriers. A thorough analysis of CLSA approaches, such as translation-based models, multilingual NLP strategies, and cross-lingual transfer learning, is presented in this research. We go over important issues including linguistic diversity, the scarcity of annotated datasets, and cultural differences in attitude. Lastly, we examine new prospects such as the creation of multilingual sentiment-aware AI systems, improved machine translation for sentiment preservation, and improvements in huge language models. The importance of cross-lingual sentiment analysis (CLSA) in a multilingual digital world is examined in this research. We go over the CLSA methods, the main language translation issues, cultural variations, and data accessibility. We also look at new approaches and potential paths forward to enhance sentiment analysis in various languages. The increasing requirement to analyses feelings in a multilingual, diversified global context has made cross-lingual sentiment analysis a crucial component of contemporary natural language processing (NLP). This work offers a thorough examination of cross-lingual sentiment analysis, examining the difficulties and methods employed to overcome linguistic and cultural differences in sentiment classification. To improve sentiment analysis across languages, the study focuses on the current approaches, the difficulties of working with low-resource languages, and the use of machine translation, transfer learning, and multilingual models. Furthermore, by using creative methods for data gathering, model adaption, and cross-lingual transfer, we highlight new trends and chances to raise the calibre of cross-lingual sentiment analysis.