Discovering sentiment sequence within email data through trajectory representation
Journal Publication ResearchOnline@JCUAbstract
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.
Journal
Expert Systems with Applications
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Volume
99
ISBN/ISSN
1873-6793
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Pages Count
11
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Publisher
Elsevier
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Publish Date
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Date
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EISSN
N/A
DOI
10.1016/j.eswa.2018.01.026