Email sSentiment analysis through k-Means labeling and support vector machine classification

Liu, Sisi;Lee, Ickjai

Abstract

Sentiment analysis for social media and online document has been a burgeoning area in text mining for the last decade. However, Email sentiment analysis has not been studied and examined thoroughly even though it is one of the most ubiquitous means of communication. In this research, a hybrid sentiment analysis framework for Email data using term frequency-inverse document frequency term weighting model for feature extraction, and k-means labeling combined with support vector machine classifier for sentiment classification is proposed. Empirical results indicate comparatively better classification results with the proposed framework than other combinations.

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Cybernetics and Systems

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1087-6553

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Taylor and Francis

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DOI

10.1080/01969722.2018.1448242