Sentiment Analysis
Sentiment analysis aims to automatically determine the emotional tone expressed in text, aiming to understand opinions and attitudes. Current research heavily focuses on leveraging large language models (LLMs) like BERT and its variants, along with other architectures such as graph neural networks, to improve accuracy and efficiency, particularly in multimodal settings and low-resource languages. This field is crucial for various applications, including market research, social media monitoring, and understanding public opinion, driving advancements in natural language processing and impacting decision-making across numerous sectors.
Papers
Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages
Khalid Alnajjar, Mika Hämäläinen, Jack Rueter
Sentiment Analysis in the Era of Large Language Models: A Reality Check
Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, Lidong Bing
Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review
Karthick Prasad Gunasekaran