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Sentiment Analysis User Guide

Learn how to use our intelligent sentiment analysis tool to understand emotional tones in text content

Features
Core features and advantages of the sentiment analysis tool

Intelligent Detection

Uses advanced NLP and machine learning to automatically identify sentiment tendencies and emotional states

Intensity Scoring

Provides sentiment intensity scores and confidence levels to help understand emotional depth

Multi-language Support

Supports multiple languages including Chinese, English, and other major languages for comprehensive analysis

Batch Processing

Can process single text or batch analysis for efficient handling of large amounts of data

Usage Steps
Follow these steps to use the sentiment analysis tool
1

Input Text

Paste the text content to be analyzed into the input box. This can be comments, feedback, social media posts, etc.

2

Select Analysis Mode

Choose between basic sentiment analysis (positive/negative/neutral) or detailed sentiment analysis (including specific emotion types).

3

Set Analysis Parameters

Adjust analysis parameters as needed:

  • Sentiment Thresholds: Set confidence requirements for analysis
  • Emotion Types: Choose specific emotions to analyze
  • Batch Size: For multiple texts, set appropriate batch size
4

Execute Analysis

Click the "Analyze Sentiment" button, and AI will process the text and generate sentiment analysis results.

5

View Results

View analysis results, including sentiment tendency, intensity scores, confidence levels, and detailed analysis reports.

Sentiment Classification
Understanding different types of sentiment analysis

Basic Sentiment Classification

Positive Sentiment
Expresses satisfaction, happiness, appreciation
Negative Sentiment
Expresses dissatisfaction, anger, disappointment
Neutral Sentiment
Objective statements with no obvious emotional tendency

Detailed Emotion Types

😊
Joy
Happiness, excitement, satisfaction
😠
Anger
Dissatisfaction, anger, annoyance
😢
Sadness
Disappointment, depression, sadness
😨
Fear
Worry, anxiety, fear
😲
Surprise
Shock, unexpected, surprise
Application Scenarios
Common use cases for sentiment analysis
🎧
Customer Service
Analyze feedback and complaint sentiments
📱
Social Media Monitoring
Monitor brand mention sentiments
📊
Market Research
Analyze consumer attitudes toward products
✍️
Content Creation
Evaluate emotional tone of articles
⭐
Product Reviews
Analyze user feedback and ratings
🏢
Brand Monitoring
Track public perception and reputation
Usage Tips
Suggestions to improve sentiment analysis quality and efficiency

Best Practices

  • Ensure complete text, avoid sentence breaks or missing content
  • Preserve original expression style, don't over-edit the text
  • For long texts, analyze in segments for better accuracy
  • Use batch analysis function to process large amounts of data efficiently

Important Notes

  • Single analysis text length is limited to 10,000 characters
  • Analysis results are for reference only, manual review is recommended for important decisions
  • Sarcasm and irony may not be fully recognized in current version
  • Consider contextual and cultural background factors when interpreting results
Frequently Asked Questions
Common questions and answers from users

Q: How accurate is sentiment analysis?

A: High accuracy on standard text, but for sarcasm, irony, or complex contexts, manual assistance may be needed.

Q: What languages are supported?

A: Mainly supports Chinese and English with high accuracy, with varying effects for other languages.

Q: How to handle sarcasm and irony?

A: Current version may not fully recognize complex sarcastic expressions, recommend combining context and manual judgment.

Q: Can text in images be analyzed?

A: Currently only supports text analysis, text in images needs to be extracted to text format first.

Ready to start analyzing sentiments?

Experience our intelligent sentiment analysis tool now

Start Analyzing