Multichannel 2D-CNN Attention-Based BiLSTM Method for Low-Resource Ewe Sentiment Analysis Article Swipe
Victor Kwaku Agbesi
,
Wenyu Chen
,
Chiagoziem C. Ukwuoma
,
Noble Arden Kuadey
,
Collinson Colin M. Agbesi
,
Chukwuebuka Joseph Ejiyi
,
Emmanuel S. A. Gyarteng
,
Gladys Wavinya Muoka
,
Anthony Mawuena Kuadey
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.47852/bonviewjdsis32021512
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.47852/bonviewjdsis32021512
The unavailability of an annotated dataset for a low-resource Ewe language makes it difficult to develop an automated system to appropriately evaluate public opinion on events, news, policies, and regulations. In this study, we collected and preprocessed a low-resourced document-level Ewe sentiment dataset based on social media comments. We used three features learned by word embeddings (Global vectors, word-to-vector, and FastText) rather than hand-crafted features. We further proposed a novel method termed MC2D-CNN+BiLSTM-Attn to detect the exact sentiment feature from the Ewe dataset. Extensive experiments indicate that the proposed method efficiently classifies various sentiments and is superior to benchmark deep learning methods. Results show that in detecting the precise sentiments from raw Ewe textual context, the BiLSTM incorporating Glove outperforms Word2Vec and FastText embedding with an accuracy of 0.727. Furthermore, Attn+BiLSTM and multichannel convolutional neural network methods incorporating the Word2Vec embedding layer perform better than Glove and FastText embedding with an accuracy of 0.848 and 0.896. In contrast, our proposed method with the same Word2Vec embedding recorded 0.949. Received: 8 August 2023 | Revised: 19 October 2023 | Accepted: 23 November 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Victor Kwaku Agbesi: Conceptualization, Methodology, Software, Formal analysis, Writing - original draft. Wenyu Chen: Supervision, Project administration. Chiagoziem C. Ukwuoma: Formal analysis, Writing - review & editing, Visualization, Project administration. Noble A. Kuadey: Investigation, Writing - review & editing, Visualization. Collinson Colin M. Agbesi: Investigation, Writing - review & editing. Chukwuebuka J. Ejiyi: Validation, Data curation. Emmanuel S. A. Gyarteng: Validation, Data curation. Gladys W. Muoka: Validation, Visualization. Anthony M. Kuadey: Software, Data curation.
Related Topics
Concepts
Word2vec
Word embedding
Computer science
Artificial intelligence
Benchmark (surveying)
Feature (linguistics)
Word (group theory)
Embedding
Context (archaeology)
Sentiment analysis
Feature vector
Deep learning
Support vector machine
Pattern recognition (psychology)
Natural language processing
Mathematics
Geography
Cartography
Philosophy
Linguistics
Archaeology
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.47852/bonviewjdsis32021512
- https://ojs.bonviewpress.com/index.php/jdsis/article/download/1512/699
- OA Status
- diamond
- Cited By
- 4
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388931222
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388931222Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.47852/bonviewjdsis32021512Digital Object Identifier
- Title
-
Multichannel 2D-CNN Attention-Based BiLSTM Method for Low-Resource Ewe Sentiment AnalysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-23Full publication date if available
- Authors
-
Victor Kwaku Agbesi, Wenyu Chen, Chiagoziem C. Ukwuoma, Noble Arden Kuadey, Collinson Colin M. Agbesi, Chukwuebuka Joseph Ejiyi, Emmanuel S. A. Gyarteng, Gladys Wavinya Muoka, Anthony Mawuena KuadeyList of authors in order
- Landing page
-
https://doi.org/10.47852/bonviewjdsis32021512Publisher landing page
- PDF URL
-
https://ojs.bonviewpress.com/index.php/jdsis/article/download/1512/699Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ojs.bonviewpress.com/index.php/jdsis/article/download/1512/699Direct OA link when available
- Concepts
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Word2vec, Word embedding, Computer science, Artificial intelligence, Benchmark (surveying), Feature (linguistics), Word (group theory), Embedding, Context (archaeology), Sentiment analysis, Feature vector, Deep learning, Support vector machine, Pattern recognition (psychology), Natural language processing, Mathematics, Geography, Cartography, Philosophy, Linguistics, Archaeology, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2024: 4Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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