EX MACHINA: Humans, Machines, Language and Semantic Technologies
Table of Content ● Semantic technologies and natural language processing ● What is text analytics and how is it related to Data? ● What is sentiment analysis and how does it work? ● Why do you need a text analytics strategy?
Overview The most significant difference between human beings and other species is our ability to learn and use rule-based languages to communicate with each other. Although essentially human, language can be subjective and ambiguous. There are many different languages that possess elements that can create slight or subtle shades of meaning through connotation and subtext. Linguistic devices such as sarcasm, idioms and slang diminish the literal meaning of words and phrases. So the ability to enable computers to correctly recognize, comprehend and extract meaning from text is the most important step toward the achievement of artificial intelligence.
Semantic technologies and natural language processing About 80% of company data is unstructured (think of chat bots, social media, emails, survey answers, support queries, customer reviews, text documents, etc.). But data structure is important for knowledge discovery and analytics. To overcome this basic challenge, data scientists use semantic technologies to turn unstructured data into high quality information and actionable knowledge, to drive optimal decision making for business. Semantic technologies are automation tools developed so computers can understand human language more quickly, accurately and at scale. Examples of semantic technologies include text mining, sentiment analysis, and semantic search. The ultimate goal of semantic technologies is to ensure that computers can identify, extract and classify meaning within data as it is expressed in text files.
What is sentiment analysis and how does it work?
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