Dandelion Package

Dandelion

• Domain: Dandelion
• Credentials: accessToken

How to get credentials:

0. Go to Dandelion website
1. Register or log in
2. Go to Dashboard to get your accessToken.

Custom datatypes:

Datatype	Description	Example
	Datepicker	String which includes date and time
	Map	String which includes latitude and longitude Array
	List	Simple array
	Select	String with predefined values
	Array	Array of objects

Dandelion.extractEntity

Automatically tag your texts, extracting Wikipedia entities and enriching your data.

Field	Type	Description
accessToken	credentials	Access token obtained from Dandelion
sourceType	Select	Type of the input: text,url,html,html_fragment
source	String	request input
lang	Select	The language of the text to be annotated. Possible values: de , en , es , fr , it , pt , auto
minConfidence	String	Confidence is a numeric estimation of the quality of the annotation, which ranges between 0 and 1.
minLength	Number	With this parameter you can remove those entities having a spot shorter than a minimum length.
socialHashtag	Boolean	With this parameter you enable special mention parsing to correctly analyze tweets and facebook posts.
include	List	Returns more information on annotated entities. Array: types, categories, abstract, image, lod, alternate_labels
extraTypes	Select	Returns more information on annotated entities. Possible values: phone, vat
country	String	This parameter specifies the country which we assume VAT and telephone numbers to be coming from. Possible values: AD, AE, AM, AO, AQ, AR, AU, BB, BR, BS, BY, CA, CH, CL, CN, CX, DE, FR, GB, HU, IT, JP, KR, MX, NZ, PG, PL, RE, SE, SG, US, YT, ZW
customSpots	String	Enable specific user-defined spots to be used when annotating the text. You can define your own spots or use someone else’s ones if they shared the spots-ID with you.
epsilon	String	This parameter defines whether the Entity Extraction API should rely more on the context or favor more common topics to discover entities. Using an higher value favors more common topics, this may lead to better results when processing tweets or other fragmented inputs where the context is not always reliable. Accepted values: 0.0 … 0.5

Dandelion.getTextSimilarity

Compare two sentences and get a score of their semantic similarity.

Field	Type	Description
accessToken	credentials	Access token obtained from Dandelion
sourceType	Select	Type of the input: text,url,html,html_fragment
source1	String	request input
source2	String	request input
lang	Select	The language of the text to be annotated. Possible values: de , en , es , fr , it , pt , auto
bow	String	The Text Similarity API normally uses a semantic algorithm for computing similarity of texts. It is possible, however, to use a more classical syntactic algorithm where the semantic one fails. This can be done with this parameter. Possible values: always , one_empty , both_empty , never

Dandelion.detectLanguages

Detects language of the source.

Field	Type	Description
accessToken	credentials	Access token obtained from Dandelion
sourceType	Select	Type of the input: text,url,html,html_fragment
source	String	Request input
clean	Boolean	Set this parameter to true if you want the text to be cleaned from urls, email addresses, hashtags, and more, before being processed.

Dandelion.identifyTextSentiments

This API analyses a text and tells whether the expressed opinion is positive, negative, or neutral. Given a short sentence, it returns a label representing the identified sentiment, along with a numeric score ranging from strongly positive (1.0) to extremely negative (-1.0).

Field	Type	Description
accessToken	credentials	Access token obtained from Dandelion
sourceType	Select	Type of the input: text,url,html,html_fragment
source	String	Request input
lang	Select	Possible values: en, it, auto

Dandelion.searchWikipages

Looking for Wikipedia pages but don’t know their exact title? We can help you to search for the page you want.

Field	Type	Description
accessToken	credentials	Access token obtained from Dandelion
text	String	Request input
lang	Select	Possible values: en, de, es, fr, it, pt
limit	Number	Restricts the output to the first N results.
query	String	With this parameter you can choose the behaviour of the search: full, prefix
include	List	Returns more information on annotated entities. Array: types, categories, abstract, image, lod, alternate_labels

Dandelion.createModel

Create a new model

Field	Type	Description
accessToken	credentials	Access token obtained from Dandelion
model	JSON	Formed model. Example: {“lang”: “en”, “description”: “basic”, “categories”: [{“name”: “sport”, “topics”: {“http://en.wikipedia.org/wiki/Sport”: 2}}] }

Dandelion.getSingleModel

Read a specific model

Field	Type	Description
accessToken	credentials	Access token obtained from Dandelion
modelId	String	Id of existing model

Dandelion.getModels

List all your models

Field	Type	Description
accessToken	credentials	Access token obtained from Dandelion

Dandelion.updateModel

Update an existing model

Field	Type	Description
accessToken	credentials	Access token obtained from Dandelion
modelId	String	Id of existing model
model	JSON	Formed model. Example: {“lang”: “en”, “description”: “basic”, “categories”: [{“name”: “sport”, “topics”: {“http://en.wikipedia.org/wiki/Sport”: 2}}] }

Dandelion.classifyText

This API classifies short documents into a set of user-defined classes.

Field	Type	Description
accessToken	credentials	Access token obtained from Dandelion
sourceType	Select	Type of the input: text,url,html,html_fragment
source	String	Request input
modelId	String	The unique ID of the model you want to use.
minScore	String	Return those categories that get a score above this threshold.

Dandelion.deleteModel

Delete a specific model

Field	Type	Description
accessToken	credentials	Access token obtained from Dandelion
modelId	String	Id of existing model