Content analysis in the context of Natural language understanding


Content analysis in the context of Natural language understanding

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⭐ Core Definition: Content analysis

Content analysis is the study of documents and communication artifacts, including texts, photos, speeches, or essays. Social scientists use content analysis to examine patterns in communication in a replicable and systematic manner. One of the key advantages of using content analysis to analyse social phenomena is their non-invasive nature, in contrast to simulating social experiences or collecting survey answers.

Practices and philosophies of content analysis vary between academic disciplines. They all involve systematic reading or observation of texts or artifacts which are assigned labels (sometimes called codes) to indicate the presence of interesting, meaningful pieces of content. By systematically labeling the content of a set of texts, researchers can analyse patterns of content quantitatively using statistical methods, or use qualitative methods to analyse meanings of content within texts.

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Content analysis in the context of Pew Research Center

The Pew Research Center (also simply known as Pew) is a nonpartisan American think tank based in Washington, D.C. It provides information on social issues, public opinion, and demographic trends shaping the United States and the world. It also conducts public opinion polling, demographic research, random sample survey research, and panel based surveys, media content analysis, and other empirical social science research.

The Pew Research Center states it does not take policy stances. It is a subsidiary of the Pew Charitable Trusts and a charter member of the American Association of Public Opinion Research's Transparency Initiative.

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Content analysis in the context of Natural-language understanding

Natural language understanding (NLU) or natural language interpretation (NLI) is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU has been considered an AI-hard problem.

There is considerable commercial interest in the field because of its application to automated reasoning, machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.

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