Detecting sarcastic expressions with deep neural networks
Zihang Huang · Applied and Computational Engineering · 2023
Following the ever increasing trend in social media such as Twitter, Facebook, and Instagram, automatic analysis of people’s conversations and languages have become a problem of great significance for businesses and governments in attempt to understand and analyze people’s habits, thoughts, and patterns towards different subjects of interests. Within the field of natural language processing, sarcasm detection has always been a difficult challenge for sentiment analysis. Recent years, there has been great interests shown by researchers towards sarcasm detection. Neural networks achieve huge success and advancements surrounding this topic, but reviews for this task are very limited and there’s a lack of comprehensive review of the development of sarcasm detection so far. Thus, this paper aims to summarize and present the various methods directed towards sarcasm detection, the progress it has made, and examination of potential problems and availability for further improvements.