Predicting Chinese Phrase-Level Sentiment Intensity in Valence-Arousal Dimensions With Linguistic Dependency Features

Keyphrases

• Valence-arousal 100%
• Phrase Level 100%
• Sentiment Intensity 100%
• Linguistic Dependency 100%
• Dependency Features 100%
• Weighted Sums 50%
• Dependency Parsing 50%
• GRU Network 50%
• Network Model 25%
• Transition-based 25%
• Parser 25%
• Modal 25%
• Graphical Models 25%
• Attention Model 25%
• Graph-based 25%
• Polarity Reversal 25%
• Treebank 25%
• Degree Adverbs 25%
• Dependency Relations 25%
• Shift-or 25%
• Inference Time 25%
• Negator 25%
• ALBERT 25%
• Dependency Structure 25%
• ELECTRA 25%
• Linguistic Modifiers 25%
• Sentiment Intensity Prediction 25%
• Stacked LSTM 25%
• XLNet 25%
• Deep Biaffine Attention 25%
• Transformer Neural Network 25%

Computer Science

• dependency parse 100%
• Linguistic Dependency 100%
• Experimental Result 50%
• Neural Network 50%
• Long Short-Term Memory Network 50%
• Bidirectional Encoder Representations From Transformers 50%