@datafire/symanto

Client library for Psycholinguistic Text Analytics

Installation and Usage

``bash
npm install --save @datafire/symanto
`
`js
let symanto = require('@datafire/symanto').create({
apiKeyHeader: ""
});

.then(data => {
console.log(data);
});
`

Description

We aim to provide the deepest understanding of people through psychology & AI

Actions

$3

Identify the purpose and writing style of a written text.

Supported Languages: [ar, de, en, es, fr, it, nl, pt, ru, tr, zh]

Returned labels:
* action-seeking
* fact-oriented
* information-seeking
* self-revealing


`js
symanto.communication({}, context)
`

#### Input
* input object
* all boolean
* body PostRequest

#### Output
* output PredictionResults

$3

Detect the emotions of the text.

Supported Langauges: [en, de, es]

Returned labels:
* anger
* joy
* love
* sadness
* surprise
* uncategorized


`js
symanto.emotion({}, context)
`

#### Input
* input object
* all boolean
* body PostRequest

#### Output
* output PredictionResults

$3

Identifies what language a text is written in. Only languages that our API supports can be analyzed.

Returned labels:
* language_code of the detected language


`js
symanto.language_detection.post({}, context)
`

#### Input
* input object
* body LanguageDetectionRequest

#### Output
* output LanguageDetectionResponse

$3

Predict the personality trait of author of any written text.

Supported Languages: [ar, de, en, es, fr, it, nl, pt, ru, tr, zh]

Returned labels:

* emotional
* rational


`js
symanto.personality({}, context)
`

#### Input
* input object
* all boolean
* body PostRequest

#### Output
* output PredictionResults

$3

Evaluate the overall tonality of the text.

Supported Languages: [en, de, es]

Returned labels:
* positive
* negative


`js
symanto.sentiment({}, context)
`

#### Input
* input object
* all boolean
* body PostRequest

#### Output
* output PredictionResults

$3

Extracts topics and sentiments and relates them.


`js
symanto.topic_sentiment.post({}, context)
`

#### Input
* input object
* domain string (values: Ecom, Employee): Provide analysis domain for better extraction (optional)
* body PostRequest

#### Output
* output TopicSentimentResponse

Definitions

$3


* LanguageDetection object
* id string: id of the text.
* text required string: the text itself.

$3

* LanguageDetectionRequest array
* items LanguageDetection

$3

* LanguageDetectionResponse array
* items LanguagePredicted

$3

* LanguagePredicted object
* detected_language required string: the detected language_code corresponding to the input text.
* id string: id of the post.

$3

* Post object
* id string: id of the post.
* language required string: language_code of the text.
* text required string: the text to be analysed.

$3

* PostPredicted object
* id string: id of the post.
* predictions required array: the list of predictions.
* items Prediction

$3

* Posts array
* items Post

$3

* Prediction object
* prediction required string: the predicted label.
* probability required number: the probability of the predicted label.

$3

* Prediction Results array
* items PostPredicted

$3

* Sentiment object
* category string
* end integer
* negationTerm string
* parentCategory string
* positive boolean
* scale number
* start integer
* text string

$3

* Topic object
* category string
* end integer
* polarity number
* start integer
* text string
* topic string

$3

* TopicSentiment object
* sentence string
* sentiment Sentiment
* topic Topic

$3

* TopicSentimentOutput object
* id string
* language string
* sentiments array
* items Sentiment
* text string
* topicSentiments array
* items TopicSentiment
* topics array
* items Topic

$3

* TopicSentimentResponse array
* items TopicSentimentOutput

$3

* ValidationError object
* loc required array
* items string
* msg required string
* type required string

$3

* ValidationErrors object
* detail array`
* items ValidationError