Quick Start
Users do not need to provide additional training data, they can directly enter text for experience and use
For user-provided Chinese texts containing subjective information, automatically identify the emotional tendencies in them, such as positive/negative emotions, etc., and provide corresponding confidence levels for user evaluation, for users to analyze popular topic, monitor public opinion and provide assistance in tasks such as consumer behavior analysis
Users do not need to provide additional training data, they can directly enter text for experience and use
Model training based on deep learning can automatically learn the semantic features of the text, and recognize the emotion of the text through semantic analysis, with high recognition accuracy
Applied to multiple fields (e-commerce, automobiles, movies, etc.)
Automatically identify and distinguish positive/negative reviews based on consumer reviews of products, helping other consumers to choose products more objectively, and at the same time helping businesses improve their products and services
For film reviews, the emotional information is automatically extracted and analyzed to determine the positive or negative of the emotional tendency of the film reviews, thereby helping to evaluate the quality of the film
Analyze the emotional tendencies in the current hot topics discussed , and judge the current public opinion orientation to conduct effective public opinion monitoring
