Textofia core analytics provides text analytics APIs are the most comprehensive set of NLP APIs for software developers.
We trained our models on a significant amount of data that provide state-of-the-art accuracy on most common NLP use-cases such as sentiment analysis, emotion detection and text summarisation.
We provide APIs for keyword/topic extraction, named entity extraction and disambiguation, document summarisation, sentiment analysis, emotion analysis, hate speech detection, relation extraction, document classification and machine translation.
Our analytics consists of a control panel dashboard to manage our text analytics API hits, subscriptions and payments.
Detects keywords and keyphrases in given text that provides complete picture of the topics discussed.
Named Entity Recognition can identify persons, organisations, places, organization and various type of entities in text.
Summarisation identifies the sentences most pertinent to a contents’ topics, and combine them to give a concise synopsis of the original source text.
It provides an analysis of the overall emotion of a text content showing whether it was postive, negative or neutral.
Emotion Analysis analyses whether the underlying emotion behind textual data is Happy, Sad, Angry, Fearful, Excited or Bored.
It analyses whether the underlying intention behind a sentence is opinion, news, marketing, complaint, suggestion, appreciation, and query.
Hate speech analysis identifies offensive language with 98% accuracy and helps in fighting online abuse and spam.
Relationship extraction is the extraction of grammatical and semantic connections between two entities in a piece of text.
Document Classification assigns text one or more categories allowing to structure data for better insights.
Translates documents from various european languages to english. eg: french, spanish, german, dutch, italian etc
With our platform businesses are able to automatically extract meaning from all sorts of unstructured data, from social media posts and emails to live chats and surveys, and turn it into quantitative insights. By identifying trends and patterns with text analytics, businesses can improve customer satisfaction detect product issues, conduct market research, and monitor brand reputation, among other things. It can analyze large volumes of data in a very short time, and also allows to obtain results in real-time.