Discovering sentiment sequence within email data through trajectory representation

Liu, Sisi, and Lee, Ickjai (2018) Discovering sentiment sequence within email data through trajectory representation. Expert Systems with Applications, 99. pp. 1-11.

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Abstract

Traditional document-level sentiment analysis fails to consider sentiment sequence within documents. This research paper proposes a novel perspective of sequence-based document sentiment analysis for discovering sentiment sequence and clustering sentiments for Email data. The proposed scheme of approach applies a trajectory clustering algorithm to Email trajectories transformed from sentiment features generated from SentiWordNet lexicon for discovering sentiment sequence within topic and temporal pattern distributions on the basis of trajectory clusters and their representative trajectories. Experiments conducted on real Email data provide evidence on proving the feasibility of the proposed technique and justifying the indispensability of sentiment sequence within documents in the determination of sentiment polarity.

Item ID: 53616
Item Type: Article (Research - C1)
ISSN: 1873-6793
Keywords: sentiment analysis; traclus; trajectory clustering; sentiment sequence
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Additional Information:

A version of this publication was included as Chapter 4 of the following PhD thesis: Liu, Sisi (2020) Document-level sentiment analysis of email data. PhD thesis, James Cook University, which is available Open Access in ResearchOnline@JCU. Please see the Related URLs for access.

Date Deposited: 20 Jul 2018 00:04
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery @ 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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