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Machine Learning Approaches for Sentiment Analysis

Machine Learning Approaches for Sentiment Analysis

Basant Agarwal, Namita Mittal
Copyright: © 2014 |Pages: 16
ISBN13: 9781466660861|ISBN10: 1466660864|EISBN13: 9781466660878
DOI: 10.4018/978-1-4666-6086-1.ch011
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MLA

Agarwal, Basant, and Namita Mittal. "Machine Learning Approaches for Sentiment Analysis." Data Mining and Analysis in the Engineering Field, edited by Vishal Bhatnagar, IGI Global, 2014, pp. 193-208. https://doi.org/10.4018/978-1-4666-6086-1.ch011

APA

Agarwal, B. & Mittal, N. (2014). Machine Learning Approaches for Sentiment Analysis. In V. Bhatnagar (Ed.), Data Mining and Analysis in the Engineering Field (pp. 193-208). IGI Global. https://doi.org/10.4018/978-1-4666-6086-1.ch011

Chicago

Agarwal, Basant, and Namita Mittal. "Machine Learning Approaches for Sentiment Analysis." In Data Mining and Analysis in the Engineering Field, edited by Vishal Bhatnagar, 193-208. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6086-1.ch011

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Abstract

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

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