Cross-Domain Sentiment Encoding through Stochastic Word Embedding Article Swipe
Yanbin Hao
,
Tingting Mu
,
Richang Hong
,
Meng Wang
,
Xueliang Liu
,
John Y. Goulermas
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/tkde.2019.2913379
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/tkde.2019.2913379
Sentiment analysis is an important topic concerning identification of feelings, attitudes, emotions and opinions from text. To automate such analysis, a large amount of example text needs to be manually annotated for model training. This is laborious and expensive, but the cross-domain technique is a key solution to reducing the cost by reusing annotated reviews across domains. However, its success largely relies on the learning of a robust common representation space across domains. In the recent years, significant effort has been invested to improve the cross-domain representation learning by designing increasingly more complex and elaborate model inputs and architectures. We support that it is not necessary to increase design complexity as this inevitably consumes more time in model training. Instead, we propose to explore the word polarity and occurrence information through a simple mapping and encode such information more accurately whilst managing lower computational costs. The proposed approach is unique and takes advantage of the stochastic embedding technique to tackle cross-domain sentiment alignment. Its effectiveness is benchmarked with over ten data tasks constructed from two review corpora and it is compared against ten classical and state-of-the-art methods.
Related Topics
Concepts
Computer science
Sentiment analysis
Word embedding
ENCODE
Word (group theory)
Representation (politics)
Domain (mathematical analysis)
Artificial intelligence
Embedding
Encoding (memory)
Machine learning
Identification (biology)
Natural language processing
Key (lock)
Theoretical computer science
Law
Politics
Biochemistry
Computer security
Philosophy
Political science
Biology
Botany
Chemistry
Mathematics
Mathematical analysis
Gene
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tkde.2019.2913379
- OA Status
- green
- Cited By
- 47
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2940589961
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2940589961Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tkde.2019.2913379Digital Object Identifier
- Title
-
Cross-Domain Sentiment Encoding through Stochastic Word EmbeddingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-04-27Full publication date if available
- Authors
-
Yanbin Hao, Tingting Mu, Richang Hong, Meng Wang, Xueliang Liu, John Y. GoulermasList of authors in order
- Landing page
-
https://doi.org/10.1109/tkde.2019.2913379Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://research.manchester.ac.uk/en/publications/9f5bbb63-78d6-4539-8997-23f247120550Direct OA link when available
- Concepts
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Computer science, Sentiment analysis, Word embedding, ENCODE, Word (group theory), Representation (politics), Domain (mathematical analysis), Artificial intelligence, Embedding, Encoding (memory), Machine learning, Identification (biology), Natural language processing, Key (lock), Theoretical computer science, Law, Politics, Biochemistry, Computer security, Philosophy, Political science, Biology, Botany, Chemistry, Mathematics, Mathematical analysis, Gene, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
47Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 6, 2023: 10, 2022: 10, 2021: 8Per-year citation counts (last 5 years)
- References (count)
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64Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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