Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network Article Swipe
Lê Hoàng Sơn
,
Akshi Kumar
,
Saurabh Raj Sangwan
,
Anshika Arora
,
Anand Nayyar
,
Mohamed Abdel-Basset
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2899260
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2899260
A large community of research has been developed in recent years to analyze social media and social networks, with the aim of understanding, discovering insights, and exploiting the available information. The focus has shifted from conventional polarity classification to contemporary application-oriented fine-grained aspects such as, emotions, sarcasm, stance, rumor, and hate speech detection in the user-generated content. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we propose a deep learning model called sAtt-BLSTM convNet that is based on the hybrid of soft attention-based bidirectional long short-term memory (sAtt-BLSTM) and convolution neural network (convNet) applying global vectors for word representation (GLoVe) for building semantic word embeddings. In addition to the feature maps generated by the sAtt-BLSTM, punctuation-based auxiliary features are also merged into the convNet. The robustness of the proposed model is investigated using balanced (tweets from benchmark SemEval 2015 Task 11) and unbalanced (approximately 40000 random tweets using the Sarcasm Detector tool with 15000 sarcastic and 25000 non-sarcastic messages) datasets. An experimental study using the training- and test-set accuracy metrics is performed to compare the proposed deep neural model with convNet, LSTM, and bidirectional LSTM with/without attention and it is observed that the novel sAtt-BLSTM convNet model outperforms others with a superior sarcasm-classification accuracy of 97.87% for the Twitter dataset and 93.71% for the random-tweet dataset.
Related Topics
Concepts
Sarcasm
Computer science
Artificial intelligence
Natural language processing
Convolutional neural network
Sentiment analysis
Binary classification
SemEval
Task (project management)
Pattern recognition (psychology)
Speech recognition
Machine learning
Support vector machine
Management
Economics
Art
Irony
Literature
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2019.2899260
- https://ieeexplore.ieee.org/ielx7/6287639/8600701/08641269.pdf
- OA Status
- gold
- Cited By
- 195
- References
- 98
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2915002815
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2915002815Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2019.2899260Digital Object Identifier
- Title
-
Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Lê Hoàng Sơn, Akshi Kumar, Saurabh Raj Sangwan, Anshika Arora, Anand Nayyar, Mohamed Abdel-BassetList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2019.2899260Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08641269.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/8600701/08641269.pdfDirect OA link when available
- Concepts
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Sarcasm, Computer science, Artificial intelligence, Natural language processing, Convolutional neural network, Sentiment analysis, Binary classification, SemEval, Task (project management), Pattern recognition (psychology), Speech recognition, Machine learning, Support vector machine, Management, Economics, Art, Irony, LiteratureTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
195Total citation count in OpenAlex
- Citations by year (recent)
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2025: 11, 2024: 36, 2023: 36, 2022: 32, 2021: 51Per-year citation counts (last 5 years)
- References (count)
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98Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| counts_by_year[4].year | 2021 |
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| publication_date | 2019-01-01 |
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| abstract_inverted_index.feature | 153 |
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