Do you have any questions? Just write us an email or ask us through the feedback section.


Requests are made using GET or POST data submissions to the API entry point. Typically, a POST method is recommended in order to overcome the parameter maximum length limit when using the GET method.



This is the endpoint to access the API.

Service Method Url
Corporate Reputation POST Console

If you are working with an on-premises installation, you will need to substitute by your own server address.


These are the supported parameters.

Name Description Values Default
key The access key is required for making requests to any of our web services. You can get a valid access key for free just by creating an account at MeaningCloud. Required
of Output format. xml
Optional. Default: of=json
txt Input text that's going to be classified. UTF-8 encoded text (plain text, HTML or XML). Required.
txtf The text format parameter specifies if the text included in the txt parameter uses markup language that needs to be interpreted (known HTML tags and HTML code will be interpreted, and unknown tags will be ignored). plain
Optional. Default: txtf=plain
lang Language chosen to carry out the analysis. es: Spanish Required.
model Classification model to use. It will define into which categories the text may be classified. BusinessRep_es: model for business reputation in Spanish. There's more information on its categories in this section. Required.
ud The user dictionary allows to include user-defined entities and concepts in the sentiment analysis. It provides a mechanism to adapt the process to focus on specific domains or on terms relevant to a user's interests, either to increase the precision in any of the domains already taken into account in our ontology to include a new one, or just to add a new semantic meaning to known terms. Name of your user dictionaries. Optional. Default: ud=""
rt This parameter indicates how reliable the text to analyze is (as far as spelling, typography, etc. are concerned), and influences how strict the engine will be when it comes to take these factors into account in the analysis. y: enabled for all resources
u: enabled just for user dictionary
n: disabled
Optional. Default: rt=n
infer Deal with inferences. This feature adds a stage to the entities extractions in which the engine tries to infere knowledge: on the one hand, it tries to resolve correferences and on the other hand, it tries to establish a relationship between companies and products in order to improve categorization. y: enabled
n: disabled
Optional. Default: infer=n