Processing Resources

Language Identification is MeaningCloud's solution for automatic language detection for texts obtained from any kind of source. Based on the franc library, which bases the language detection process on N-grams, more than 160 languages are correctly identified, helping to automatize multilingual sourced applications either by correcting a manual detection preprocessing or by avoiding it altogether.

Output

The output of the system will depend on whether you defined the input parameters or not:

• If you selected inputASName or annotationTypes, the output will be included as new Features in the Annotations that match your query.
• If you left inputASName and annotationTypes unset, the output will be included as new Document Features inside each processed document.

API Documentation

If you need more information about the parameters, please refer to the Language Identification API v2.0.

Text Classification is MeaningCloud's solution for automatic text classification according to pre-established categories defined in a model. The algorithm used combines statistic classification with rule-based filtering, which allows to obtain a high degree of precision for very different environments.

Output

The output of the system will depend on wheter you defined the input parameters or not:

• If you selected inputASName or annotationTypes, the output will be included as new Features in the Annotations that match your query.
• If you left inputASName and annotationTypes unset, the output will be included as new Document Features inside each processed document.

API Documentation

If you need more information about the parameters, please refer to the Text Classification API v1.1.

Topics Extraction is MeaningCloud's solution for extracting the different elements present in sources of information. This detection process is carried out by combining a number of complex natural language processing techniques that allow to obtain morphological, syntactic and semantic analyses of a text and use them to identify different types of significant elements.

Output

First level objects of the response are tagged as new annotations. Nested objects are flattened and added as new features of these annotations.

API Documentation

If you need more information about the parameters, please refer to the Topics Extraction API v2.0.

Sentiment Analysis is MeaningCloud's solution for performing a detailed multilingual sentiment analysis of texts from different sources.

The text provided is analyzed to determine if it expresses a positive/negative/neutral sentiment; to do this, the local polarity of the different sentences in the text is identified and the relationship between them evaluated.

Output

These response objects are tagged as new annotations:

• sentence
• segment
• polarity_term
• sentimented_entity
• sentimented_concept

Also, sentiment analysis at document level is returned as new document features.

API Documentation

If you need more information about the parameters, please refer to the Sentiment Analysis API v2.1.