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Prominent Feature Extraction for Sentiment AnalysisMachine Learning Approach for Sentiment Analysis

Prominent Feature Extraction for Sentiment Analysis: Machine Learning Approach for Sentiment... [Machine learning algorithms have been widely used for sentiment analysis [66]. The bag-of-words (BoW) representation is commonly used for sentiment analysis [63, 93]. BoW method assumes the independence of words and ignores the importance of semantic and subjective information in the text. All the words in the text are considered equally important. The BoW representation is commonly used for sentiment analysis, resulting into high dimensionality of the feature space. Machine learning algorithms reduce this high-dimensional feature space with the help of feature selection techniques which selects only important features by eliminating the noisy and irrelevant features. Recently, machine learning-based sentiment analysis models are gaining prominence in the field [66].] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Prominent Feature Extraction for Sentiment AnalysisMachine Learning Approach for Sentiment Analysis

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-25341-1
Pages
21 –45
DOI
10.1007/978-3-319-25343-5_3
Publisher site
See Chapter on Publisher Site

Abstract

[Machine learning algorithms have been widely used for sentiment analysis [66]. The bag-of-words (BoW) representation is commonly used for sentiment analysis [63, 93]. BoW method assumes the independence of words and ignores the importance of semantic and subjective information in the text. All the words in the text are considered equally important. The BoW representation is commonly used for sentiment analysis, resulting into high dimensionality of the feature space. Machine learning algorithms reduce this high-dimensional feature space with the help of feature selection techniques which selects only important features by eliminating the noisy and irrelevant features. Recently, machine learning-based sentiment analysis models are gaining prominence in the field [66].]

Published: Dec 15, 2015

Keywords: Sentiment Analysis; Existing Feature Selection Techniques; mRMR Feature Selection Method; Composite Feature Set; Semantic Orientation Value

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