How are the positive instances annotated? e.g. by annotators, or by laughter from the audience?
By laughter from the audience
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.
The ability to make effective presentations has been found to be linked with success at school and in the workplace. Humor plays an important role in successful public speaking, e.g., helping to reduce public speaking anxiety often regarded as the most prevalent type of social phobia, generating shared amusement to boost persuasive power, and serving as a means to attract attention and reduce tension BIBREF0 . Automatically simulating an audience's reactions to humor will not only be useful for presentation training, but also improve conversational systems by giving machines more empathetic power. The present study reports our efforts in recognizing utterances that cause laughter in presentations. These include building a corpus from TED talks and using Convolutional Neural Networks (CNNs) in the recognition. The remainder of the paper is organized as follows: Section SECREF2 briefly reviews the previous related research; Section SECREF3 describes the corpus we collected from TED talks; Section SECREF4 describes the text classification methods; Section SECREF5 reports on our experiments; finally, Section SECREF6 discusses the findings of our study and plans for future work.