Advancements in Natural Language Processing for Multilingual Sentiment Analysis

Thesis Abstract

Abstract

The analysis of sentiment in multilingual content has gained significant importance in the era of global communication and diverse linguistic expressions. This research delves into the advancements in natural language processing (NLP) techniques for multilingual sentiment analysis. The study commences with an extensive review of sentiment analysis in NLP, focusing on the challenges and opportunities presented by multilingual data. The methodology encompasses data collection, preprocessing, the selection and implementation of multilingual NLP models and algorithms, and the design of cross-lingual sentiment analysis techniques. Performance evaluation metrics, ethical considerations, and experimentation setup are integral components of the research methodology. The implementation phase involves the deployment of multilingual NLP models, comparative analysis of cross-lingual sentiment analysis techniques, and visualization of results. The study concludes with a summary of research findings, implications for multilingual sentiment analysis advancements, future research directions, ethical considerations, and regulatory compliance in multilingual NLP. This research provides insights into the potential of NLP advancements to enhance multilingual sentiment analysis, with implications for cross-cultural communication, user acceptance, and real-world applications.