Sequence encoding incorporated CNN model for Email document sentiment classification

Liu, Sisi, and Lee, Ickjai (2021) Sequence encoding incorporated CNN model for Email document sentiment classification. Applied Soft Computing, 102. 107104.

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Abstract

Document sentiment classification is an area of study that has been developed for decades. However, sentiment classification of Email data is rather a specialized field that has not yet been thoroughly studied. Compared to typical social media and review data, Email data has characteristics of length variance, duplication caused by reply and forward messages, and implicitness in sentiment indicators. Due to these characteristics, existing techniques are incapable of fully capturing the complex syntactic and relational structure among words and phrases in Email documents.

In this study, we introduce a dependency graph-based position encoding technique enhanced with weighted sentiment features, and incorporate it into the feature representation process. We combine encoded sentiment sequence features with traditional word embedding features as input for a revised deep CNN model for Email sentiment classification. Experiments are conducted on three sets of real Email data with adequate label conversion processes. Empirical results indicate that our proposed SSE-CNN model obtained the highest accuracy rate of 88.6%, 74.3% and 82.1% for three experimental Email datasets over other comparative state-of-the-art algorithms. Furthermore, our performance evaluations on the preprocessing and sentiment sequence encoding justify the effectiveness of Email preprocessing and sentiment sequence encoding with dependency-graph based position and SWN features on the improvement of Email document sentiment classification.

Item ID: 68462
Item Type: Article (Research - C1)
ISSN: 1872-9681
Keywords: Sentiment analysis; CNN model; Sequence encoding; Graph-based position encoding
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Copyright Information: © 2021 Elsevier B.V. All rights reserved.
Date Deposited: 22 Jun 2021 22:39
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery @ 90%
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 10%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 50%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50%
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