Abstract: Human beings often use figurative language during communication to express their thoughts. Uncovering the meaning out of figurative language is not as simple as literal language. Humor identification is considered to be an important linguistic device for sentiment analysis of figurative text because it can often change the sentiments of the text. Moreover, during verbal communication people use facial expressions, gestures and other modalities to convey their feeling and to automatically understand the meaning out of figurative sentences using these modalities is part of computer vision and digital image processing. It is difficult for written sentences where facial expressions, gestures, other modalities, and emotions are absent and so it is an interesting question of research. Humor is a figurative device and a creative linguistic phenomenon. To understand the meaning of humor, we need to correctly understand the mood and emotions conveyed in the text, which is beyond the semantics of literal language communication. In this work, we have addressed these issues of understanding the emotions using affect-based information from text with various well established machine learning classifiers. We have exploited various affective content that inhibits the emotions and feeling of a writer such as emoticons, writing styles like punctuation, capitalization, sentiment words and so on. The proposed affect-based humor identification model is evaluated on the SemEval 2017 HashTagWars dataset and yelp review dataset with different types of the experimental configuration. This evaluates the effectiveness of the proposed humor identification model with different types of features.
Keywords: Humor identification, affective computing, natural language processing, machine learning
