Experimenting with a dataset of approximately 1.6M user comments from a Greek news sports portal, we explore how a state of the art RNN-based moderation method can be improved by adding user embeddings, user type embeddings, user biases, or user type biases. We observe improvements in all cases, with user embeddings leading to the biggest performance gains.
detect sarcasm in tweets. Their best system uses a word-based Convolutional Neural Network ( ). The feature vector produced by the (representing the content of the tweet) is concatenated with the user embedding of the author, and passed on to an that classifies the tweet as sarcastic or not. This method outperforms a previous state of the art sarcasm detection method @cite_4 that relies on an classifier with hand-crafted content and user-specific features. We use an instead of a , and we feed the comment and user embeddings to a simpler layer (Eq. ), instead of an . discard unknown users, unlike our experiments, and consider only sarcasm, whereas moderation also involves profanity, hate speech, bullying, threats etc.
@cite_4
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Sarcasm requires some shared knowledge between speaker and audience; it is a profoundly contextual phenomenon. Most computational approaches to sarcasm detection, however, treat it as a purely linguistic matter, using information such as lexical cues and their corresponding sentiment as predictive features. We show that by including extra-linguistic information from the context of an utterance on Twitter — such as properties of the author, the audience and the immediate communicative environment — we are able to achieve gains in accuracy compared to purely linguistic features in the detection of this complex phenomenon, while also shedding light on features of interpersonal interaction that enable sarcasm in conversation.