How to use domestic large models for sentiment analysis and user emotion insight?
When needing to use domestic large models for sentiment analysis and user emotion insight, it can usually be achieved through three core steps: data preprocessing, model invocation and parameter configuration, and result analysis and application. Data preprocessing stage: It is necessary to collect user-generated content (such as text reviews, social media messages, customer service dialogues, etc.), perform cleaning and denoising (filtering irrelevant symbols, correcting typos), extract key emotion trigger words (such as "satisfied", "disappointed", "angry"), and unify the text format to adapt to the model input requirements. Model invocation and configuration: Mainstream domestic large models (such as Baidu Wenxin Yiyan, Alibaba Tongyi Qianwen, etc.) provide sentiment analysis API interfaces. By setting sentiment polarity classification (positive/negative/neutral), emotion intensity thresholds (such as 0-10 points to quantify the intensity of emotions), and domain adaptation parameters (such as vertical scenarios like e-commerce, finance, etc.), batch text sentiment labeling can be achieved. Result analysis and application: In addition to basic sentiment classification, complex emotions can be identified in combination with context (such as "satisfied with product functions but dissatisfied with logistics speed"), and emotion distribution heat maps (showing high-frequency emotion types) or time trend reports (tracking emotion change rules) can be generated to provide a basis for product optimization and marketing strategy adjustment. It is recommended to first select typical user data samples to test the model accuracy, adjust the analysis dimensions according to the business scenario (such as e-commerce focusing on "product quality" emotions, education industry focusing on "course experience" feedback), and gradually improve the accuracy and practicality of emotion insight.
