analyzed-conversation-viewer

the target of this component is to gather the analysis resulting from the different services in one place to give the user the needed information for each sentence and the overall conversation.

Props

{ sentences: SentenceType[] }

{
 onText?: string;
 offText?: string;
 hidePii?: string;
 piiDescription?: string;
 sentimentHeader?: string;
 transcriptHeader?: string;
 sentimentDescription?: string;
}

{
 onText: 'On',
 offText: 'Off',
 hidePii: 'Hide PII',
 sentimentHeader: 'Sentiment',
 transcriptHeader: 'Transcript',
 piiDescription: 'Sensitive information across several pre-defined categories is detected and redacted from the transcript.',
 sentimentDescription: 'Sensitive information across several pre-defined categories is detected and redacted from the transcript.'
}

## SentenceType

{
 minutes: number;
 seconds: number;
}

## NamedEntity

## SentimentTypes

SentimentTypes = 'positive' | 'negative' | 'neutral'

Examples

Here is a usage example:

1const conversationResult = {
2  sentences: [
3    {
4      sentiment: "positive",
5      speakerTitle: "Speaker 1",
6      duration: { minutes: 0, seconds: 2 },
7      text: "Hi thank you for calling Bank of Contoso. Hello my name is Mary. How can I help you today?",
8      redactedText: "Hi thank you for calling Bank of Contoso. Hello my name is ****. How can I help you today?",
9      entities: [
10        { category: "Person", confidenceScore: 0.9, length: 4, offset: 59, text: "Mary" },
11        { category: "Organization", confidenceScore: 0.9, length: 15, offset: 25, text: "Bank of Contoso" },
12      ],
13    },
14    {
15      entities: [],
16      sentiment: "neutral",
17      speakerTitle: "Speaker 2",
18      duration: { minutes: 0, seconds: 2 },
19      text: "I would like to know my remaining balance in my checking account.",
20      redactedText: "I would like to know my remaining balance in my checking account.",
21    },
22  ],
23};
24
25return <AnalyzedConversationViewer result={conversationResult} />;

and the view will be like:

the nice annotations are made by the annotator library

Related topics

• Conversation sentiment analysis
• Conversation summarization
• Conversation PII
• react-text-annotator