Emotion

This model tries to determine the emotion conveyed by the writer on a text, using Ekman's list of emotions as potential results.

Although detecting emotion can be very subjective, this model could find patterns after analyzing over 210,000 samples of labeled text of comments made on social media.

Prediction labels

Limits

The maximum length accepted is 512 characters.

Emotion	Emoji	Example
anger	😠	Oh, how DARE you discuss this in public?
joy	🥳	She’s like a kewpie doll with them. Precious.
disgust	🤢	That is odd.
fear	😨	Hahaha, ok. I was worried there for a second
sadness	😔	You got banned for participating in a brigade.
surprise	😮	Oh whoops, I misread the original comment
neutral	😐	Let me give you a hint: THEY PLAY IN BOSTON!

Invokation

cURL

curl -L -G 'http://api.textkit.ai/detect/emotion' \
    --data-urlencode 'text=yeah, man! Go!' \
    --header 'X-API-Key: your_api_key_here' 

Python

import requests

url = "https://api.textkit.ai/detect/emotion?text=yeah, man! Go!"

payload={}
headers = {
  'X-API-Key': 'your_api_key_here'
}

response = requests.request("GET", url, headers=headers, data=payload)

print(response.text)

PHP

<?php

$curl = curl_init();

$url = "https://api.textkit.ai/detect/language?text=" . urlencode("yeah, man! Go!");

curl_setopt_array($curl, array(
  CURLOPT_URL => $url,
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => '',
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 0,
  CURLOPT_FOLLOWLOCATION => true,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => 'GET',
  CURLOPT_HTTPHEADER => array(
    'X-API-Key: your_api_key_here'
  ),
));

$response = curl_exec($curl);

curl_close($curl);
echo $response;

Response

{
    "prediction": {
        "emotion": "joy",
        "emoji": "🥳"
    },
    "confidence": "0.577",
    "time_ms": 989
}

Field	Meaning
prediction	The predicted label. See above for reference
confidence	Value between 0 and 1 that indicates how confident the model is
time_ms	Time in milliseconds the model took to predict the label. It does not account for the network round trip time between request and response