A hybrid sentiment analysis framework for large email data

Liu, Sisi, and Lee, Ickjai (2015) A hybrid sentiment analysis framework for large email data. In: Proceedings of 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, pp. 324-330. From: 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, 24-27 November 2015, Taipei.

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Abstract

Sentiment analysis for online text documents has been a burgeoning field of text mining among researchers and scholars for the past few decades. Nevertheless, sentiment analysis on large Email data, a ubiquity means of social networking and communication, has not been studied thoroughly. This paper proposes a framework for Email sentiment analysis using a hybrid scheme of algorithms combined with Kmeans clustering and support vector machine classifier. The evaluation for the framework is conducted through the comparison among three labeling methods, including SentiWordNet labeling, Kmeans labeling, and Polarity labeling, and five classifiers, including Support Vector Machine, Naïve Bayes, Logistic Regression, Decision Tree and OneR. Empirical results indicate a relatively high classification accuracy with proposed framework in comparison with other approaches.

Item ID: 42512
Item Type: Conference Item (Refereed Research Paper - E1)
Keywords: text mining; Kmeans; support vector machines
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ISBN: 978-1-4673-9322-5
Date Deposited: 10 Feb 2016 03:03
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890201 Application Software Packages (excl. Computer Games) @ 100%
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