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.

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.

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.

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.

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.