Liu, Sisi (2020) Document-level sentiment analysis of email data. PhD thesis, James Cook University.

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Abstract

Sisi Liu investigated machine learning methods for Email document sentiment analysis. She developed a systematic framework that has been qualitatively and quantitatively proved to be effective and efficient in identifying sentiment from massive amount of Email data. Analytical results obtained from the document-level Email sentiment analysis framework are beneficial for better decision making in various business settings.

Item ID:	65310
Item Type:	Thesis (PhD)
Keywords:	machine learning, email, sentiment analysis, email sentiment analysis, document-level sentiment analysis
Related URLs:	https://researchonline.jcu.edu.au/53616/ https://researchonline.jcu.edu.au/63010/
Copyright Information:	© 2020 Sisi Liu.
Additional Information:	Two publications arising from this thesis are stored in ResearchOnline@JCU, at the time of processing. Please see the Related URLs. The publications are: [Chapter 4] Liu, Sisi, and Lee, Ickjai (2018) Discovering sentiment sequence within email data through trajectory representation. Expert Systems with Applications, 99. pp. 1-11. [Chapter 6] Liu, Sisi, Lee, Kyungmi, and Lee, Ickjai (2020) Document-level multi-topic sentiment classification of email data with BiLSTM and data augmentation. Knowledge Based Systems, 197. 105918.
Date Deposited:	14 Dec 2020 05:20
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 > 890299 Computer Software and Services not elsewhere classified @ 100%
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