Detecting Sarcasm

Even in the early days of reading consumer reactions to product communications  we knew it was useful to have an idea of how much sarcasm was being used.  To determine human reactions to statements or images.   Often we just scrubbed potential sarcasm. but we knew we were losing something by not analyzing it.

This work on Detecting Sarcasm  also Discussed in  VentureBeat  

Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection

By Hongliang Pan, Zheng Lin, Peng Fu, Yatao Qi, Weiping Wang

Abstract

Sarcasm is a pervasive phenomenon in today’s social media platforms such as Twitter and Reddit. These platforms allow users to create multi-modal messages, including texts, images, and videos. Existing multi-modal sarcasm detection methods either simply concatenate the features from multi modalities or fuse the multi modalities information in a designed manner. However, they ignore the incongruity character in sarcastic utterance, which is often manifested between modalities or within modalities. Inspired by this, we propose a BERT architecture-based model, which concentrates on both intra and inter-modality incongruity for multi-modal sarcasm detection. To be specific, we are inspired by the idea of self-attention mechanism and design inter-modality attention to capturing inter-modality incongruity. In addition, the co-attention mechanism is applied to model the contradiction within the text. The incongruity information is then used for prediction. The experimental results demonstrate that our model achieves state-of-the-art performance on a public multi-modal sarcasm detection dataset. ...