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Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text.It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources."
Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Thus, a representation that compresses the storage size of a file from 10 MB to 2 MB yields a space saving of 1 - 2/10 = 0.8, often notated as a percentage, 80%. For signals of indefinite size, such as streaming audio and video, the compression ratio is defined in terms of uncompressed and compressed data rates instead of data sizes:
Several of these approaches are based upon the concept of online analytical processing, or OLAP, and may be supported by data models such as text cubes. [14] Once document metadata is available through a data model, generating summaries of subsets of documents (i.e., cells within a text cube) may be performed with phrase-based approaches. [15]
It is a general-purpose learner and its ability to perform the various tasks was a consequence of its general ability to accurately predict the next item in a sequence, [2] [7] which enabled it to translate texts, answer questions about a topic from a text, summarize passages from a larger text, [7] and generate text output on a level sometimes ...
The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
A number of term-weighting schemes have derived from tf–idf. One of them is TF–PDF (term frequency * proportional document frequency). [14] TF–PDF was introduced in 2001 in the context of identifying emerging topics in the media. The PDF component measures the difference of how often a term occurs in different domains.
Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, [87] and information value theory. [88] These tools include models such as Markov decision processes , [ 89 ] dynamic decision networks , [ 90 ] game theory and mechanism design .