Deep Learning for Sarcasm Identification in News Headlines
Sarcasm is a mode of expression whereby individuals communicate their positive or negative sentiments through words contrary to their intent. This communication style is prevalent in news headlines and social media platforms, making it increasingly challenging for individuals to detect sarcasm accur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/9/5586 |
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| author | Rasikh Ali Tayyaba Farhat Sanya Abdullah Sheeraz Akram Mousa Alhajlah Awais Mahmood Muhammad Amjad Iqbal |
| author_facet | Rasikh Ali Tayyaba Farhat Sanya Abdullah Sheeraz Akram Mousa Alhajlah Awais Mahmood Muhammad Amjad Iqbal |
| author_sort | Rasikh Ali |
| collection | DOAJ |
| description | Sarcasm is a mode of expression whereby individuals communicate their positive or negative sentiments through words contrary to their intent. This communication style is prevalent in news headlines and social media platforms, making it increasingly challenging for individuals to detect sarcasm accurately. To mitigate this challenge, developing an intelligent system that can detect sarcasm in headlines and news is imperative. This research paper proposes a deep learning architecture-based model for sarcasm identification in news headlines. The proposed model has three main objectives: (1) to comprehend the original meaning of the text or headlines, (2) to learn the nature of sarcasm, and (3) to detect sarcasm in the text or headlines. Previous studies on sarcasm detection have utilized datasets of tweets and employed hashtags to differentiate between ordinary and sarcastic tweets depending on the limited dataset. However, these datasets were prone to noise regarding language and tags. In contrast, using multiple datasets in this study provides a comprehensive understanding of sarcasm detection in online communication. By incorporating different types of sarcasm from the Sarcasm Corpus V2 from Baskin Engineering and sarcastic news headlines from The Onion and HuffPost, the study aims to develop a model that can generalize well across different contexts. The proposed model uses LSTM to capture temporal dependencies, while the proposed model utilizes a GlobalMaxPool1D layer for better feature extraction. The model was evaluated on training and test data with an accuracy score of 0.999 and 0.925, respectively. |
| first_indexed | 2024-03-11T04:24:32Z |
| format | Article |
| id | doaj.art-c6e1c65c761a4cebab038c90c7fc04e8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T04:24:32Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c6e1c65c761a4cebab038c90c7fc04e82023-11-17T22:36:04ZengMDPI AGApplied Sciences2076-34172023-04-01139558610.3390/app13095586Deep Learning for Sarcasm Identification in News HeadlinesRasikh Ali0Tayyaba Farhat1Sanya Abdullah2Sheeraz Akram3Mousa Alhajlah4Awais Mahmood5Muhammad Amjad Iqbal6Faculty of Computer Science and Information Technology, The Superior University, Lahore 54600, PakistanFaculty of Computer Science and Information Technology, The Superior University, Lahore 54600, PakistanFaculty of Computer Science and Information Technology, The Superior University, Lahore 54600, PakistanFaculty of Computer Science and Information Technology, The Superior University, Lahore 54600, PakistanComputer Science and Information Systems Department, Applied Computer Science College, King Saud University, Riyadh 12571, Saudi ArabiaComputer Science and Information Systems Department, Applied Computer Science College, King Saud University, Riyadh 12571, Saudi ArabiaFaculty of Information Technology, University of Central Punjab, Lahore 54100, PakistanSarcasm is a mode of expression whereby individuals communicate their positive or negative sentiments through words contrary to their intent. This communication style is prevalent in news headlines and social media platforms, making it increasingly challenging for individuals to detect sarcasm accurately. To mitigate this challenge, developing an intelligent system that can detect sarcasm in headlines and news is imperative. This research paper proposes a deep learning architecture-based model for sarcasm identification in news headlines. The proposed model has three main objectives: (1) to comprehend the original meaning of the text or headlines, (2) to learn the nature of sarcasm, and (3) to detect sarcasm in the text or headlines. Previous studies on sarcasm detection have utilized datasets of tweets and employed hashtags to differentiate between ordinary and sarcastic tweets depending on the limited dataset. However, these datasets were prone to noise regarding language and tags. In contrast, using multiple datasets in this study provides a comprehensive understanding of sarcasm detection in online communication. By incorporating different types of sarcasm from the Sarcasm Corpus V2 from Baskin Engineering and sarcastic news headlines from The Onion and HuffPost, the study aims to develop a model that can generalize well across different contexts. The proposed model uses LSTM to capture temporal dependencies, while the proposed model utilizes a GlobalMaxPool1D layer for better feature extraction. The model was evaluated on training and test data with an accuracy score of 0.999 and 0.925, respectively.https://www.mdpi.com/2076-3417/13/9/5586sarcasmsarcasm detectionnews headlinessentiment analysisneural network (NN)deep learning (DL) |
| spellingShingle | Rasikh Ali Tayyaba Farhat Sanya Abdullah Sheeraz Akram Mousa Alhajlah Awais Mahmood Muhammad Amjad Iqbal Deep Learning for Sarcasm Identification in News Headlines Applied Sciences sarcasm sarcasm detection news headlines sentiment analysis neural network (NN) deep learning (DL) |
| title | Deep Learning for Sarcasm Identification in News Headlines |
| title_full | Deep Learning for Sarcasm Identification in News Headlines |
| title_fullStr | Deep Learning for Sarcasm Identification in News Headlines |
| title_full_unstemmed | Deep Learning for Sarcasm Identification in News Headlines |
| title_short | Deep Learning for Sarcasm Identification in News Headlines |
| title_sort | deep learning for sarcasm identification in news headlines |
| topic | sarcasm sarcasm detection news headlines sentiment analysis neural network (NN) deep learning (DL) |
| url | https://www.mdpi.com/2076-3417/13/9/5586 |
| work_keys_str_mv | AT rasikhali deeplearningforsarcasmidentificationinnewsheadlines AT tayyabafarhat deeplearningforsarcasmidentificationinnewsheadlines AT sanyaabdullah deeplearningforsarcasmidentificationinnewsheadlines AT sheerazakram deeplearningforsarcasmidentificationinnewsheadlines AT mousaalhajlah deeplearningforsarcasmidentificationinnewsheadlines AT awaismahmood deeplearningforsarcasmidentificationinnewsheadlines AT muhammadamjadiqbal deeplearningforsarcasmidentificationinnewsheadlines |