Cognitive-inspired domain adaptation of sentiment lexicons

• Main Authors: Xing, Frank Z., Pallucchini, Filippo, Cambria, Erik
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Sentiment Lexicon; Domain Adaptation
• Online Access: https://hdl.handle.net/10356/151125
• Institution: Nanyang Technological University

Sentiment lexicons are essential tools for polarity classification and opinion mining. In contrast to machine learning methods that only leverage text features or raw text for sentiment analysis, methods that use sentiment lexicons embrace higher interpretability. Although a number of domain-specifi...