Author: Zen and the Art of Computer Programming
"13. "Use emotional analysis to improve user experience: Make AI more warm""
introduction
With the rapid development of artificial intelligence technology, sentiment analysis, as an important artificial intelligence technology, has gradually been applied in various fields. Sentiment analysis is a natural language processing technology that helps us better understand and grasp the needs and emotions of users by judging and classifying the emotional tendencies of texts.
This article will introduce how to use sentiment analysis technology to improve user experience and how to make AI more warm. This article will first introduce the basic concepts, technical principles and implementation steps of sentiment analysis, then explain it through application examples and code implementation, and finally optimize and improve it, and attach common questions and answers.
Technical principles and concepts
2.1. Explanation of basic concepts
Sentiment analysis is a natural language processing technology that extracts the emotional characteristics of text by judging and classifying the emotional tendency of text. Sentiment analysis technology mainly includes sentiment classification, sentiment polarity analysis, sentiment intensity analysis, etc.
2.2. Introduction to technical principles: algorithm principles, operating steps, mathematical formulas, etc.
Sentiment analysis technology is mainly implemented through the following algorithms:
Sentiment classification: Classify text sentiment into positive sentiment, negative sentiment, or neutral sentiment. Commonly used emotion classification algorithms include: logistic regression, support vector machine, naive Bayes, K-Nearest Neighbors, etc.
Sentiment polarity analysis: Divide text sentiment into positive sentiment or negative sentiment, or into two types: positive sentiment and negative sentiment. Commonly used sentiment polarity analysis algorithms include: Sentiiment Analysis, Polarity Sentiment Analysis, Improved Polarity Sent