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
Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis
Sebastian-Vasile Echim, Răzvan-Alexandru Smădu, Andrei-Marius Avram, Dumitru-Clementin Cercel, Florin Pop
Detect Depression from Social Networks with Sentiment Knowledge Sharing
Yan Shi, Yao Tian, Chengwei Tong, Chunyan Zhu, Qianqian Li, Mengzhu Zhang, Wei Zhao, Yong Liao, Pengyuan Zhou
Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis
Daniela Teodorescu, Saif M. Mohammad
Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews
Yukyung Lee, Jaehee Kim, Doyoon Kim, Yookyung Kho, Younsun Kim, Pilsung Kang