# Predicting Audience's Laughter Using Convolutional Neural Network

arXiv: https://arxiv.org/abs/1702.02584 · HTML: https://mlsanity.com/paper/1702.02584/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2017.

## Built on by (most cited first, 4 of 4)

- [When 'YES' Meets 'BUT': Can Large Models Comprehend Contradictory Humor Through Comparative Reasoning?](https://mlsanity.com/paper/2503.23137/) (2025)
- [Mining Effective Features Using Quantum Entropy for Humor Recognition](https://mlsanity.com/paper/2302.03716/) (2023)
- [This joke is [MASK]: Recognizing Humor and Offense with Prompting](https://mlsanity.com/paper/2210.13985/) (2022)
- [CaRGo-T: Causal Reasoning Graph-of-Thought improves Multimodal Humor Comprehension](https://mlsanity.com/paper/2608.23172/) (2026)

## Abstract (arXiv)

For the purpose of automatically evaluating speakers' humor usage, we build a presentation corpus containing humorous utterances based on TED talks. Compared to previous data resources supporting humor recognition research, ours has several advantages, including (a) both positive and negative instances coming from a homogeneous data set, (b) containing a large number of speakers, and (c) being open. Focusing on using lexical cues for humor recognition, we systematically compare a newly emerging text classification method based on Convolutional Neural Networks (CNNs) with a well-established conventional method using linguistic knowledge. The advantages of the CNN method are both getting higher detection accuracies and being able to learn essential features automatically.