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analyzeSentimentWithOpinionMining.ts
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analyzeSentimentWithOpinionMining.ts
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// Copyright (c) Microsoft Corporation.
// Licensed under the MIT License.
/**
* This sample demonstrates how to analyze sentiment on a more granular level,
* mining individual assessments from reviews (also known as aspect-based
* sentiment analysis). In the example below, we analyze reviews about a hotel
* for sentiment and different assessments about targets in the reviews.
*
* @summary analyzes the sentiment of a piece of text and mine opinions about
* different targets
*/
import { TextAnalyticsClient, AzureKeyCredential } from "@azure/ai-text-analytics";
// Load the .env file if it exists
import * as dotenv from "dotenv";
dotenv.config();
// You will need to set these environment variables or edit the following values
const endpoint = process.env["ENDPOINT"] || "<cognitive services endpoint>";
const apiKey = process.env["TEXT_ANALYTICS_API_KEY"] || "<api key>";
const documents = [
{
text: "The food and service were unacceptable, but the concierge were nice",
id: "0",
language: "en",
},
{
text: "The rooms were beautiful but dirty. The AC was good and quiet, but the elevator was broken",
id: "1",
language: "en",
},
{
text: "The breakfast was good, but the toilet was smelly",
id: "2",
language: "en",
},
{
text: "Loved this hotel - good breakfast - nice shuttle service.",
id: "3",
language: "en",
},
{
text: "I had a great unobstructed view of the Microsoft campus",
id: "4",
language: "en",
},
];
export async function main() {
console.log("=== Opinion Mining Sample ===");
const client = new TextAnalyticsClient(endpoint, new AzureKeyCredential(apiKey));
const results = await client.analyzeSentiment(documents, { includeOpinionMining: true });
for (let i = 0; i < results.length; i++) {
const result = results[i];
console.log(`- Document ${result.id}`);
if (!result.error) {
console.log(`\tDocument text: ${documents[i].text}`);
console.log(`\tOverall Sentiment: ${result.sentiment}`);
console.log("\tSentiment confidence scores:", result.confidenceScores);
console.log("\tSentences");
for (const { sentiment, confidenceScores, opinions } of result.sentences) {
console.log(`\t- Sentence sentiment: ${sentiment}`);
console.log("\t Confidence scores:", confidenceScores);
console.log("\t Mined opinions");
for (const { target, assessments } of opinions) {
console.log(`\t\t- Target text: ${target.text}`);
console.log(`\t\t Target sentiment: ${target.sentiment}`);
console.log("\t\t Target confidence scores:", target.confidenceScores);
console.log("\t\t Target assessments");
for (const { text, sentiment } of assessments) {
console.log(`\t\t\t- Text: ${text}`);
console.log(`\t\t\t Sentiment: ${sentiment}`);
}
}
}
} else {
console.error(`\tError: ${result.error}`);
}
}
}
main().catch((err) => {
console.error("The sample encountered an error:", err);
});