COMPARITIVE STUDY OF ALGORITHMS

Effective sentiment analysis of multilingual social media data is crucial for grasping user sentiments across various linguistic contexts. This research explores the challenges and advancements in sentiment analysis techniques, particularly in environments with limited resources. While most studies focus on monolingual analyses, recent developments in deep learning, especially transformer models, have shown promise for multilingual applications. The study evaluates different frameworks for sentiment analysis, emphasizing essential steps such as data collection, preparation, feature extraction, and model selection to address linguistic diversity. It also examines various methods, including artificial intelligence techniques and multilingual approaches, assessing their effectiveness in low-resource settings. The goal is to validate the robustness of these frameworks and identify best practices for accurate sentiment analysis in constrained environments, ultimately enhancing global sentiment understanding through adaptable and advanced techniques. Understanding user sentiments on social media is vital across diverse languages, especially in resource-limited situations. Although most research has centered on monolingual contexts, advancements in deep learning transformers have demonstrated effectiveness. Major social media platforms like Twitter and Facebook play a key role in extracting valuable insights from their vast and evolving data. This study investigates and evaluates cutting-edge sentiment analysis techniques, focusing on their performance in low-resource linguistic environments where data availability is limited. By conducting a comparative analysis of various models, the research seeks to confirm the robustness of these frameworks and identify the most effective techniques for sentiment analysis under linguistic constraints.