Sentiment-oriented Sarcasm Integration for Video Sentiment Analysis Enhancement with Sarcasm Assistance

Junlin Fang, Wenya Wang, Guosheng Lin, Fengmao Lv

Abstract: Sarcasm is an intricate expression phenomenon and has garnered increasing attentions over the recent years, especially for multimodal contexts such as videos. Nevertheless, despite being a significant aspect of human sentiment, the effect of sarcasm is consistently overlooked in sentiment analysis. Videos with sarcasm often convey sentiments that diverge or even contradict their explicit messages. Prior works mainly concentrate on simply modeling sarcasm and sentiment features by utilizing the Multi-Task Learning (MTL) framework, which we found introduces detrimental interplays between the sarcasm detection task and sentiment analysis task. Therefore, this study explores the effective enhancement of video sentiment analysis through the incorporation of sarcasm information. To this end, we propose the Progressively Sentiment-oriented Sarcasm Refinement and Integration (PS2RI) framework, which focuses on modeling sentiment-oriented sarcasm features to enhance sentiment prediction. Instead of naively combining sarcasm detection and sentiment prediction under an MTL framework, PS2RI iteratively performs the sentiment-oriented sarcasm refinement and sarcasm integration operations within the sentiment recognition framework, in order to progressively learn sarcasm-aware sentiment feature without suffering the detrimental interplays caused by information irrelevant to the sentiment analysis task. Extensive experiments are conducted to validate the effectiveness of our approach. Code is available at https://github.com/tiggers23/PS2RI.