Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

• Published in: Security and communication networks Vol. 2021; pp. 1 - 12
• Main Author: Li, Xiang
• Format: Journal Article
• Language: English
• Published: London Hindawi 17.11.2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Classification; Data mining; Deep learning; Dictionaries; Emotions; Feature extraction; German language; International relations; Machine learning; Morphology; Sentiment analysis; Social networks; Subject specialists
• ISSN: 1939-0114; 1939-0122; 1939-0122
• DOI: 10.1155/2021/8309586

In this paper, we propose a multilevel feature representation method that combines word-level features, such as German morphology and slang, and sentence-level features, such as special symbols and English-translated sentiment information, and build a deep learning model for German sentiment classification based on the self-attentive mechanism, in order to address the characteristics of German social media texts that are colloquial, irregular, and diverse. Compared with the existing studies, this model not only has the most obvious improvement effect but also has better feature extraction and classification ability for German emotion.