Abstract

Humor detection is one of the most popular tasks in natural language processing. Yet humor is abstract to numerate, and there isn’t an acknowledged standard for humor assessment. Toward this end, this paper explores the performance of CNN, RNN, BiLSTM in tackling humor detection. We use the regression method and classification method, respectively, to identify the best model. The experiment was conducted on a dataset that is composed of 15,000 news headlines. Results show that the CNN network is a preferable choice, and combined with the classification method, the model obtains the best performance. Though there is plenty of more sophisticated sentiment analysis models, our work offers an intuition for short text sentiment analysis.

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