Do you want to try SentiLecto's Brazilian Portuguese version?

SentiLecto is Natural Tech's NLU engine. This Entity-based Sentiment Analysis solution yields a highly fine-grained representation for the sentiment values involved in each opinion. Unlike other approaches, this solution can deal with polarity shifting in the same sentence ('I like chocolate but I hate strawberry ice-cream'), within embedded clauses ('Norwegians, who are an aggresive People, export the exquisite herring'), or even onto the very same word ('Somebody who wasted a chance to do something' means that person did something bad about something good). SentiLecto better represents the premise whereby the entities involved in the opinion are syntactically mapped onto SVO (subject-verb-object) slots for their sentiment assignments: 'Mary hates John' (2 entities but only the object has a negative presentation) vs. 'Mary defames John' (the same 2 entities but only the subject has negative presentation). This Entity-based Sentiment Analysis solution yields a highly fine-grained representation for the sentiment values involved in each opinion. Unlike other approaches, this solution can deal with polarity shifting in the same sentence ('I like chocolate but I hate strawberry ice-cream'), within embedded clauses ('Norwegians, who are an aggresive People, export the exquisite herring'), or even onto the very same word ('Somebody who wasted a chance to do something' means that person did something bad about something good). SentiLecto better represents the premise whereby the entities involved in the opinion are syntactically mapped onto SVO (subject-verb-object) slots for their sentiment assignments: 'Mary hates John' (2 entities but only the object has a negative presentation) vs. 'Mary defames John' (the same 2 entities but only the subject has negative presentation).

SentiLecto leans on outstanding linguistic features such as: passive/active voice transformation, negation scope, anaphora resolution and co-reference chains, ellipsis, modality treatment, semantic features (animity and others) and accurate verbal frames for all Spanish and Portuguese verbs, even with 'se-impersonal' usages ('se mostraron retratos' = 'alguien mostró retratos' = 'somebody showed portraits'), 'se-clitic' usages (for example, plain action 'mostrar' 'to show something' vs. 'mostrarSE' 'to show yourself, namely to feel some way before a situation').

It can recognize & classify named-entities (NERC) with identity matching. Also, SentiLecto can identify whether or not an utterance is a real fact (fact mining), normalizing facts through deep understanding of syntax and semantics.

Try our demo with this challenging input text, or you can enter any complex large text, such as news, or even raw datasets of tweets. Copy & paste the entire body text from any recent news into SentiLecto's input text box and behold the results! While there might be some occasional misinterpretations in sparse utterances, after processing large data sets of converging texts in a few seconds -for example, all news about the same topic-, the most important facts and opinionated entities will certainly emerge through an accurate and exhaustive linguistic representation.

Still skeptical? Take a look at the following showcase with an interesting insight about a raw comparison among different editorial lines of newspapers covering related news on politicians from Argentina.

SentiLecto is being used to automatically generate this blog with more than 300 high-quality posts on a daily basis, rewriting and enriching content and, more interestingly, merging news covering the same facts. This is just a show case of SentiLecto's NLU capabilities.

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