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情感分析
Analyze a given text to determine its sentiment, providing a detailed breakdown of positive, negative, and neutral elements, along with an overall sentiment score and analysis of undertones.
-
连接 ID - 在 Integration Service 中建立的连接。 访问下拉菜单以选择、添加或管理连接。
- Text - The text to be analyzed for sentiment. This field supports
String
type input.
管理属性
使用“管理属性”向导配置或使用对象的任何标准或自定义字段。 您可以选择字段以将其添加到活动画布中。 添加的标准或自定义字段可在“属性”面板(在 Studio Desktop 中)或在“显示其他属性”下(在 Studio Web 中)下找到。
其他属性
输出
- Overall Sentiment - Contains the sentiment score and label:
- Very Negative: -99 to -67
- Negative: -66 to -34
- Slightly Negative: -33 to -1
- Neutral: 0
- Slightly Positive: 1 to 33
- Positive: 34 to 66
- Very Positive: 67 to 99
- Sentiment Breakdown - Counts of positive, negative, neutral, and total statements.
- Confidence Level - The overall confidence level of the analysis.
- Key Phrases - A string of key phrases identified in the sentiment analysis.
- Analysis - Detailed explanation of the sentiment analysis.
- Undertones - Subtle undertones detected in the text with their impact.
- Sentiment Analysis - Automatically generated output variable.
Output JSON format:
{
"overallSentiment": {
"score": 0,
"label": ""
},
"sentimentBreakdown": {
"positiveStatements": 0,
"negativeStatements": 0,
"neutralStatements": 0,
"totalStatements": 0
},
"confidenceLevel": 0,
"keyPhrases": [
{
"phrase": "",
"sentiment": "",
"confidence": 0
}
],
"analysis": "",
"undertones": [
{
"description": "",
"impact": ""
}
]
}
{
"overallSentiment": {
"score": 0,
"label": ""
},
"sentimentBreakdown": {
"positiveStatements": 0,
"negativeStatements": 0,
"neutralStatements": 0,
"totalStatements": 0
},
"confidenceLevel": 0,
"keyPhrases": [
{
"phrase": "",
"sentiment": "",
"confidence": 0
}
],
"analysis": "",
"undertones": [
{
"description": "",
"impact": ""
}
]
}
-
The complexity and length of the input text may affect the accuracy of the analysis.
-
The system may have difficulties with highly context-dependent or culturally specific expressions.
-
Sarcasm, irony, and other forms of figurative language may pose challenges for accurate sentiment detection.
-
The system's performance may vary across different languages or dialects.