MeaningCloud
Integrate MeaningCloud's text analytics into your workflow. Several APIs with classic Natural Language Processing (NLP) tasks are included: Topics Extraction, Lemmatization, Classification, Deep Categorization and Sentiment Analysis. We offer 10 languages
Text Clustering is MeaningCloud's solution for automatic document clustering, i.e., the task of grouping a set of texts in such a way that texts in the same group (called a cluster) are more similar to each other than to those in other clusters.
By MeaningCloud
Updated 3 weeks ago
Summarization is MeaningCloud's solution to extract a summary for a given document, selecting the most relevant sentences in it to try to sum up what it is about.
This process does not take into account the language when it evaluates the importance of a sentence, which means that it's language independent and can be applied to documents in any language.
By MeaningCloud
Updated 3 weeks ago
The Document Structure Analysis extracts different sections of a given document with markup content (which includes formatted documents such as PDF or Microsoft Word files), including the title, headings, abstract and parts of an email.
This process, even though it takes into account some language markers, is based mainly in the markup of the document, so it can be applied to documents in any language.
By MeaningCloud
Updated 3 weeks ago
Deep Categorization is MeaningCloud's solution for in-depth rule-based categorization. It assigns one or more categories to a text, using a very detailed rule-based language that allows you to identify very specific scenarios and patterns using a combination of morphological, semantic and text rules.
By MeaningCloud
Updated 3 weeks ago
This service provides detailed linguistic information for a given text in English, Spanish, French, Italian, Portuguese and Catalan.
There are three operating modes that cover different aspects of the morphosyntactic and semantic analysis: Lemmatization, which provides the lemmas of the different words in a text; PoS tagging: which provides not only the grammatical category of a word, including semantic information about that word; Syntactic analysis: that provides a thorough syntactic analysis, giving a complete syntactic tree where the leaves represent the most basic elements and their morphological and semantic analyses.
By MeaningCloud
Updated 3 weeks ago
Automatic language detection for texts obtained from any kind of source (blog, twitter, online news and so on). Through statistic techniques based on N-grams evaluation, more than 60 languages are correctly identified.
By MeaningCloud
Updated 3 weeks ago
Topics Extraction tags locations, people, companies, dates and many other elements appearing in a text written in Spanish, English, French, Italian, Portuguese or Catalan. 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.
By MeaningCloud
Updated 3 weeks ago
Automatic multilingual 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. Three models available: IPTC (International Press Telecommunications Council standard), EuroVocs and Corporate Reputation model. Languages covered are Spanish, English, French, Italian, Portuguese and Catalan.
By MeaningCloud
Updated 3 weeks ago
Multilingual sentiment analysis of texts from different sources (blogs, social networks,...).
Besides polarity at sentence and global level, Sentiment Analysis uses advanced natural language processing techniques to also detect the polarity associated to both entities and concepts in the text. Sentiment Analysis also gives the user the possibility of detecting the polarity of user-defined entities and concepts, making the service a flexible tool applicable to any kind of scenario. Additionally, Sentiment Analysis detects if the text processed is subjective or objective and if it contains irony marks [beta], both at global and sentence level, giving the user additional information about the reliability of the polarity obtained from the sentiment analysis.
By MeaningCloud
Updated 3 weeks ago