Name
Abstract | ||
|---|---|---|
The content on social media now-a-days includes a huge number of messages both in textual and visual forms that are satirical in nature. Satire in the form of irony, sarcasm, and ridicule to a person, is depicted by memes on social media. Satire detection in text is an active area of research, but in the visual domain it is relatively less explored. The objective of our work is detection of satire in images taken from the popular photo-sharing platform - Flickr. Traditional methods for visual satire detection are based on supervised learning which has a necessary requirement of annotating the data which is a tedious task. In our work, to address this issue, we propose a novel, unsupervised approach which leverages the visual semantics of the images. We provide a study of clustering methods, where the difference between visual semantics of the two classes - satirical and non-satirical - becomes the basis for classification of visual content. Here, we suggest an autoencoder based clustering framework to effectively combine embedded feature learning and clustering assignments for detection of satire.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3368567.3368582 | Proceedings of the 11th Forum for Information Retrieval Evaluation |
Keywords | Field | DocType |
auto encoders, satire detection, social media, unsupervised learning | Irony,Sarcasm,Autoencoder,Computer science,Supervised learning,Unsupervised learning,Artificial intelligence,Natural language processing,Cluster analysis,Semantics,Feature learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-7750-8 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aman Sinha | 1 | 0 | 1.35 |
| Parth Patekar | 2 | 0 | 0.34 |
| Radhika Mamidi | 3 | 0 | 0.68 |