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Sentiment Analysis allows you to extract emotions and feelings in a given string of text. Also called Opinion Mining, it uses Natural Language Processing (NLP), text analysis and computational linguistics to identify and detect subjective information from the input text.
NER (also called entity identification or entity extraction) is an information extraction technique that automatically identifies named entities in a text (places, people, brands, and events) and classifies them into predefined categories.
Keyword extraction (also known as keyword detection or keyword analysis) is a text analysis technique that consists of automatically extracting the most important words and expressions in a text or a document.
Language Detection automatically defines the most likely language in which a text or document is expressed.
Summarization is the process of reducing a text to a shorter form while keeping the most important information.
Question Answering generates an answer to a question based on a set of documents. This is useful for question-answering applications on sources of truth, like company documentation or a knowledge base.
Semantic Search allows you to do a semantic search over a set of documents. This means that you can provide a query, such as a natural language question or a statement, and the provided documents will be scored and ranked based on how semantically related they are to the input query.
Syntax Analysis (also called Parsing) is used to carry out a syntax analysis of a given text to reveal the syntactic components and their grammatical relationships.
Text Anonymization's intent is privacy protection. It is the process of removing personally identifiable information from text so that the people described by the data remain anonymous.