AI Text Sentiment Analysis

Introduction

This API provides a powerful tool for analyzing the sentiment of text data. It helps you understand the overall sentiment (positive, negative, or neutral) and detailed sentiment breakdowns (joy, sadness, anger, etc.) within your text.

Authentication

This API is currently stateless and does not require any form of authentication.

Base URL

https://ai-text-sentiment-analysis.onrender.com

Endpoints

Analyze Text (POST /analyze_text)

Analyzes the sentiment of the provided text, highlighting sentiments within sentences, calculating an overall sentiment score, and providing detailed sentiment counts.

Request

• Method: POST
• Endpoint: /analyze_text
• Content-Type: application/json

Request Body

Parameters

• text: (required, string): The text for which you want to analyze sentiment. Maximum length is 2000 characters.

{
  "text": "Text to be analyzed for sentiment."
}

Status Codes

• 200 OK: Successful analysis.
• 400 Bad: Request: Invalid request format or missing required parameters.
• 500 Internal Server Error: Unexpected error during analysis.

Response

Success Response (200 OK)

{
  "sentiment": "Positive",
  "highlighted_text": {
    "Sentence 1": "joy",
    "Sentence 2": "sadness",
    
  },
  "overall_sentiment_score": 75.0,
  "sentiment_scores": {
    "joy": 3,
    "sadness": 2,
    "fear": 0,
    "neutral": 1,
    "surprise": 0,
    "anger": 0,
    "shame": 0,
    "disgust": 0
  },
  "sentiment_label": "Positive"
}

Response Fields

• sentiment: Overall sentiment of the analyzed text (Positive or Negative).
• highlighted_text: Sentences with sentiments highlighted.
• overall_sentiment_score: Overall sentiment score between 0 (negative) and 100 (positive).
• sentiment_scores: Detailed counts of each sentiment type within the text.
• sentiment_label: Overall sentiment label (Positive or Negative).

Example Usage

cURL

curl -X POST \
  https://ai-text-sentiment-analysis.onrender.com/analyze_text \
  -H 'Content-Type: application/json' \
  -d '{
    "text": "Text to be analyzed for sentiment."
  }'

Example Response

{
  "sentiment": "Positive",
  "highlighted_text": {
    "Sentence 1": "joy",
    "Sentence 2": "sadness",
    
  },
  "overall_sentiment_score": 75.0,
  "sentiment_scores": {
    "joy": 3,
    "sadness": 2,
    "fear": 0,
    "neutral": 1,
    "surprise": 0,
    "anger": 0,
    "shame": 0,
    "disgust": 0
  },
  "sentiment_label": "Positive"
}

Python Requests

import requests

url = "https://ai-text-sentiment-analysis.onrender.com/analyze_text"
payload = {
  "text": "Text to be analyzed for sentiment."
}
headers = {
  "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

Example Response

{
  "sentiment": "Positive",
  "highlighted_text": {
    "Sentence 1": "joy",
    "Sentence 2": "sadness",
    
  },
  "overall_sentiment_score": 75.0,
  "sentiment_scores": {
    "joy": 3,
    "sadness": 2,
    "fear": 0,
    "neutral": 1,
    "surprise": 0,
    "anger": 0,
    "shame": 0,
    "disgust": 0
  },
  "sentiment_label": "Positive"
}

JavaScript (Node.js)

const fetch = require('node-fetch');

const url = 'https://ai-text-sentiment-analysis.onrender.com/analyze_text';
const data = { text: 'Text to be analyzed for sentiment.' };

fetch(url, {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify(data),
})
  .then((response) => response.json())
  .then((data) => console.log(data))
  .catch((error) => console.error('Error:', error));

Example Response

{
  "sentiment": "Positive",
  "highlighted_text": {
    "Sentence 1": "joy",
    "Sentence 2": "sadness",
    
  },
  "overall_sentiment_score": 75.0,
  "sentiment_scores": {
    "joy": 3,
    "sadness": 2,
    "fear": 0,
    "neutral": 1,
    "surprise": 0,
    "anger": 0,
    "shame": 0,
    "disgust": 0
  },
  "sentiment_label": "Positive"
}

PHP

<?php

$url = 'https://ai-text-sentiment-analysis.onrender.com/analyze_text';
$data = array('text' => 'Text to be analyzed for sentiment.');

$options = array(
    'http' => array(
        'header'  => "Content-Type: application/json\r\n",
        'method'  => 'POST',
        'content' => json_encode($data)
    )
);

$context  = stream_context_create($options);
$result = file_get_contents($url, false, $context);
$response = json_decode($result);

var_dump($response);

?>

Example Response

{
  "sentiment": "Positive",
  "highlighted_text": {
    "Sentence 1": "joy",
    "Sentence 2": "sadness",
    
  },
  "overall_sentiment_score": 75.0,
  "sentiment_scores": {
    "joy": 3,
    "sadness": 2,
    "fear": 0,
    "neutral": 1,
    "surprise": 0,
    "anger": 0,
    "shame": 0,
    "disgust": 0
  },
  "sentiment_label": "Positive"
}

Java

// Make sure to import necessary libraries
import org.apache.http.HttpResponse;
import org.apache.http.client.HttpClient;
import org.apache.http.client.methods.HttpPost;
import org.apache.http.entity.StringEntity;
import org.apache.http.impl.client.HttpClientBuilder;

import java.io.BufferedReader;
import java.io.InputStreamReader;

public class SentimentAnalysis {

    public static void main(String[] args) {
        try {
            HttpClient httpClient = HttpClientBuilder.create().build();
            HttpPost request = new HttpPost("https://ai-text-sentiment-analysis.onrender.com/analyze_text");
            StringEntity params = new StringEntity("{\"text\":\"Text to be analyzed for sentiment.\"}");
            request.addHeader("content-type", "application/json");
            request.setEntity(params);
            HttpResponse response = httpClient.execute(request);
            BufferedReader reader = new BufferedReader(new InputStreamReader(response.getEntity().getContent()));
            String line;
            StringBuffer result = new StringBuffer();
            while ((line = reader.readLine()) != null) {
                result.append(line);
            }
            System.out.println(result.toString());
        } catch (Exception ex) {
            ex.printStackTrace();
        }
    }
}

Example Response

{
  "sentiment": "Positive",
  "highlighted_text": {
    "Sentence 1": "joy",
    "Sentence 2": "sadness",
    
  },
  "overall_sentiment_score": 75.0,
  "sentiment_scores": {
    "joy": 3,
    "sadness": 2,
    "fear": 0,
    "neutral": 1,
    "surprise": 0,
    "anger": 0,
    "shame": 0,
    "disgust": 0
  },
  "sentiment_label": "Positive"
}

Error Handling

• 400 Bad Request: If the request body is missing or malformed.
• 500 Internal Server Error: If an unexpected error occurs on the server.

Additional Notes

You can send multiple requests to analyze different pieces of text. The API utilizes machine learning for sentiment analysis, and the results may not always be perfect. Consider incorporating the results alongside your own judgment for comprehensive analysis. The API is constantly under development, and new features may be added in the future.

Conclusion

With the AI Text Sentiment Analysis API, you can effortlessly analyze the sentiment of textual content, gaining valuable insights into the emotions conveyed within the text. Integrate this API into your applications to enhance user experiences, optimize content strategies, and make data-driven decisions.

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Created by developer for developers.