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 (Refereed Research - C1)
Keywords: sentiment analysis; traclus; trajectory clustering; sentiment sequence
ISSN: 1873-6793
Date Deposited: 20 Jul 2018 00:04
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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