Correlation feature selection

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Correlation feature selection
The correlation feature selection (CFS) measure evaluates subsets of features on the basis of the following hypothesis: "Good feature subsets contain features highly correlated with the classification, yet uncorrelated to each other".[33][34]
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https://en.wikipedia.org/wiki/Feature_selection#Correlation_feature_selection
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Semantic analysis

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Semantic analysis describes the process of understanding natural language–the way that humans communicate–based on meaning and context. ... It analyzes context in the surrounding text and it analyzes the text structure to accurately disambiguate the proper meaning of words that have more than one definition
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https://expertsystem.com/natural-language-process-semantic-analysis-definition/
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Word embedding

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Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with many dimensions per word to a continuous vector space with a much lower dimension.

Methods to generate this mapping include neural networks,[1] dimensionality reduction on the word co-occurrence matrix,[2][3][4] probabilistic models,[5] explainable knowledge base method,[6] and explicit representation in terms of the context in which words appear.[7]

Word and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing[8] and sentiment analysis.[9]
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https://en.wikipedia.org/wiki/Word_embedding
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Support vector machine

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In machine learning, support-vector machines (SVMs, also support-vector networks^[1]) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.

Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting).

An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible.

New examples are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall.

In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required, which attempts to find natural clustering of the data to groups, and then map new data to these formed groups.

The support-vector clustering^[2] algorithm, created by Hava Siegelmann and Vladimir Vapnik, applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data, and is one of the most widely used clustering algorithms in industrial applications.^[citatio

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Sentiment Classification

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Given a set of evaluative documents D, it determines whether each document d ? D expresses a positive or negative opinion (or sentiment) on an object. Learn more in: Opinion Mining and Information Retrieval: Techniques for E-Commerce

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Classifying the polarity of a given text in the document, sentence, or feature/aspect level; e.g., emotional states. Learn more in: Sentiment Analysis in Supply Chain Management

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https://www.igi-global.com/dictionary/sentiment-classification/26512

Text mining

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According to Hotho et al. (2005) we can differ three different perspectives of text mining, namely text mining as information extraction, text mining as text data mining, and text mining as KDD (Knowledge Discovery in Databases) process.^[1] 

Text mining is "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources."^[2] 

Written resources can be websites, books, emails, reviews, articles.

Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. 

High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. 

Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 

'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. 

Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).

Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. 

The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP), different types of algorithms and analytical methods. 

An important phase of this process is the interpretation of the gathered information.

A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. 

The document is the basic element while starting with text mining. Here, we define a document as a unit of textual data, which normally exists in many types of collections.^[3]

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https://en.wikipedia.org/wiki/Text_mining

singular value decomposition

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In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix that generalizes the eigendecomposition of a square normal matrix to any ${\displaystyle m\times n}$ matrix via an extension of the polar decomposition.


Mathematical applications of the SVD include computing the pseudoinverse, matrix approximation, and determining the rank, range, and null space of a matrix. The SVD is also extremely useful in all areas of science, engineering, and statistics, such as signal processing, least squares fitting of data, and process control.

Animated illustration of the SVD of a 2D, real shearing matrix M. First, we see the unit disc in blue together with the two canonical unit vectors. We then see the actions of M, which distorts the disk to an ellipse. The SVD decomposes M into three simple transformations: an initial rotation V^*, a scaling ${\displaystyle \mathbf {\Sigma } }$ along the coordinate axes, and a final rotation U. The lengths σ₁ and σ₂ of the semi-axes of the ellipse are the singular values of M, namely Σ_1,1 and Σ_2,2.

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https://en.wikipedia.org/wiki/Singular_value_decomposition