Aspect Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) aims to identify sentiments expressed towards specific aspects or features within text, going beyond overall sentiment classification. Current research heavily utilizes transformer-based models, often incorporating techniques like graph convolutional networks and attention mechanisms to capture complex relationships between aspects, opinions, and sentiments, addressing challenges like long-range dependencies and handling multi-modal data (text and images). ABSA's detailed insights are valuable for various applications, including customer feedback analysis, market research, and improving product development, driving ongoing advancements in natural language processing and related fields.
Papers
Deep Learning Brasil at ABSAPT 2022: Portuguese Transformer Ensemble Approaches
Juliana Resplande Santanna Gomes, Eduardo Augusto Santos Garcia, Adalberto Ferreira Barbosa Junior, Ruan Chaves Rodrigues, Diogo Fernandes Costa Silva, Dyonnatan Ferreira Maia, Nádia Félix Felipe da Silva, Arlindo Rodrigues Galvão Filho, Anderson da Silva Soares
RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis
Xusheng Zhao, Hao Peng, Qiong Dai, Xu Bai, Huailiang Peng, Yanbing Liu, Qinglang Guo, Philip S. Yu