Advancing Humor-Focused Sentiment Analysis through Improved Contextualized Embeddings and Model Architecture
This survey addresses the challenge of understanding humor for humor-aware language processing models, which is increasingly important for virtual assistants and IoT devices.
This paper surveys current research on improving humor-focused sentiment analysis by incorporating contextualized and nonverbal elements into language models. It explores techniques for enhanced contextualized embeddings that include nonverbal information and new deep architectures designed to improve context retention.
Humor is a natural and fundamental component of human interactions. When correctly applied, humor allows us to express thoughts and feelings conveniently and effectively, increasing interpersonal affection, likeability, and trust. However, understanding the use of humor is a computationally challenging task from the perspective of humor-aware language processing models. As language models become ubiquitous through virtual-assistants and IOT devices, the need to develop humor-aware models rises exponentially. To further improve the state-of-the-art capacity to perform this particular sentiment-analysis task we must explore models that incorporate contextualized and nonverbal elements in their design. Ideally, we seek architectures accepting non-verbal elements as additional embedded inputs to the model, alongside the original sentence-embedded input. This survey thus analyses the current state of research in techniques for improved contextualized embedding incorporating nonverbal information, as well as newly proposed deep architectures to improve context retention on top of popular word-embeddings methods.