Sarcasm Discernment on Social Media Platform
Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag-based supervision but such datasets are noisy in terms of labels and language. To overcome the limitations related to noise in Twitter datasets, this News Headlines dataset for Sarcasm Detection is collecte...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2021-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/85/e3sconf_icmed2021_01037.pdf |
| _version_ | 1831766054210109440 |
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| author | Sagarika Namasani Sreenija Reddy Bommadi Varshitha Vanka Geetanjali Kodavati Ganapathi Raju N V Kunaparaju Latha |
| author_facet | Sagarika Namasani Sreenija Reddy Bommadi Varshitha Vanka Geetanjali Kodavati Ganapathi Raju N V Kunaparaju Latha |
| author_sort | Sagarika Namasani |
| collection | DOAJ |
| description | Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag-based supervision but such datasets are noisy in terms of labels and language. To overcome the limitations related to noise in Twitter datasets, this News Headlines dataset for Sarcasm Detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events and we collected all the headlines from News in Brief and News in Photos categories (which are sarcastic). We collect real (and non-sarcastic) news headlines from Huff Post. Sarcasm Detection on social media platform. The dataset is collected from two news websites, theonion.com and huffingtonpost.com. Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings. Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, we get high-quality labels with much less noise as compared to Twitter datasets. Unlike tweets that reply to other tweets, the news headlines obtained are self-contained. |
| first_indexed | 2024-12-22T06:14:39Z |
| format | Article |
| id | doaj.art-168e28ecf67348129c1a582a1fac62c7 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-22T06:14:39Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-168e28ecf67348129c1a582a1fac62c72022-12-21T18:36:07ZengEDP SciencesE3S Web of Conferences2267-12422021-01-013090103710.1051/e3sconf/202130901037e3sconf_icmed2021_01037Sarcasm Discernment on Social Media PlatformSagarika NamasaniSreenija Reddy BommadiVarshitha VankaGeetanjali KodavatiGanapathi Raju N VKunaparaju LathaPast studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag-based supervision but such datasets are noisy in terms of labels and language. To overcome the limitations related to noise in Twitter datasets, this News Headlines dataset for Sarcasm Detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events and we collected all the headlines from News in Brief and News in Photos categories (which are sarcastic). We collect real (and non-sarcastic) news headlines from Huff Post. Sarcasm Detection on social media platform. The dataset is collected from two news websites, theonion.com and huffingtonpost.com. Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings. Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, we get high-quality labels with much less noise as compared to Twitter datasets. Unlike tweets that reply to other tweets, the news headlines obtained are self-contained.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/85/e3sconf_icmed2021_01037.pdf |
| spellingShingle | Sagarika Namasani Sreenija Reddy Bommadi Varshitha Vanka Geetanjali Kodavati Ganapathi Raju N V Kunaparaju Latha Sarcasm Discernment on Social Media Platform E3S Web of Conferences |
| title | Sarcasm Discernment on Social Media Platform |
| title_full | Sarcasm Discernment on Social Media Platform |
| title_fullStr | Sarcasm Discernment on Social Media Platform |
| title_full_unstemmed | Sarcasm Discernment on Social Media Platform |
| title_short | Sarcasm Discernment on Social Media Platform |
| title_sort | sarcasm discernment on social media platform |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/85/e3sconf_icmed2021_01037.pdf |
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