Dimensionality Reduction Techniques for Text Mining

Dimensionality Reduction Techniques for Text Mining

Neethu Akkarapatty (SCMS School of Engineering and Technology, India), Anjaly Muralidharan (SCMS School of Engineering and Technology, India), Nisha S. Raj (SCMS School of Engineering and Technology, India) and Vinod P. (SCMS School of Engineering and Technology, India)
Copyright: © 2017 |Pages: 24
DOI: 10.4018/978-1-5225-0489-4.ch003
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Abstract

Sentiment analysis is an emerging field, concerned with the analysis and understanding of human emotions from sentences. Sentiment analysis is the process used to determine the attitude/opinion/emotions expressed by a person about a specific topic based on natural language processing. Proliferation of social media such as blogs, Twitter, Facebook and Linkedin has fuelled interest in sentiment analysis. As the real time data is dynamic, the main focus of the chapter is to extract different categories of features and to analyze which category of attribute performs better. Moreover, classifying the document into positive and negative category with fewer misclassification rate is the primary investigation performed. The various approaches employed for feature selection involves TF-IDF, WET, Chi-Square and mRMR on benchmark dataset pertaining diverse domains.
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Sentiment Classification

Sentiment classification is a part of opinion mining which refers to the task of extracting sentiment word from a given text and then classifying the content into positive or negative in its sentiment. Classification is usually performed at three levels namely:

  • 1.

    Document.

  • 2.

    Sentence.

  • 3.

    Attribute level.

In the following section, each of them will be discussed in detail.

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