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Intellexer Platform  semantic tools for sentiment analysis named entity extraction summarization categorization and text analytics

Intellexer Platform semantic tools for sentiment analysis named entity extraction summarization categorization and text analytics

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Active countries Wolrdwide, United States, European Union

Description

Intellexer Sentiment Analyzer is the powerful and efficient solution which automatically detects sentiment (positivity/negativity), opinion objects and emotions (liking, anger, disgust and etc.) from unstructured text information. Intellexer Sentiment Analyzer can be effectively used to: capture customer’s expectations, attitudes and aversions; discover emerging trends, analyze feedback on marketing campaigns; understand product issues in real-time, increase revenue and profits from vendor services, gather about competitors activities, profile customer preferences, realize what product or service is better and why.
Intellexer Summarizer analyzes the document, extracts its main ideas and puts them into a short summary or creates annotation.
Intellexer Categorizer puts unsorted documents into pre-defined categories. Categorizer automatically classifies documents and assigns to user categories based on the easy training stage.
Intellexer Named Entity Recognizer is a solution that extracts names (named entities) and defines relations between them. The recognized types of named entities include personal names, titles, positions (occupation), names of organizations, geographical locations, etc.
The team of R&D department of EffectiveSoft Ltd. has developed a unique hybrid approach to linguistic analysis which, unlike existing methods, is based on using not only linguistic and statistical information, but also the set of complex semantic rules. This technique provides the one of the best accuracy results on the market and the system adaptation to the knowledge domain.
Our core module – Intellexer Linguistic Processor performs syntactical and semantic analysis of English texts. It effectively parses the input text and extracts the main constituents in a sentence, such as noun phrases, verb phrases, adjectival phrases, and adverbial phrases. As a result, a tree-like structure of the input sentence is formed. The tree considers diverse semantic relations between the constituents: Location, Tightness, Direction, Degree, Effectiveness, Structure, for example.
Semantic technology transfers the machine language into meaningful language. It does not matter what words you use or in what order you use the words to describe your idea. Our semantic tools analyze your information by considering a variety of possible semantic meanings for each word in the sentence. Taking into account the entire context, our tools are able to understand the general idea and then give you pertinent answers.

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