Abstract
With the online presence of more than half the world population, social media plays a very important role in the lives of individuals as well as businesses alike. Social media enables businesses to advertise their products, build brand value, and reach out to their customers. To leverage these social media platforms, it is important for businesses to process customer feedback in the form of posts and tweets. Sentiment analysis is the process of identifying the emotion, either positive, negative or neutral, associated with these social media texts. The presence of sarcasm in texts is the main hindrance in the performance of sentiment analysis. Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant, with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions make sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. The multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text. We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media. Models trained using our proposed approach are easily interpretable and enable identifying sarcastic cues in the input text which contribute to the final classification score. We visualize the learned attention weights on a few sample input texts to showcase the effectiveness and interpretability of our model.
Highlights
Sarcasm is a rhetorical way of expressing dislike or negative emotions using exaggerated language constructs
With the explosion of internet usage, sarcasm detection in online communications from social networking platforms, discussion forums, and e-commerce websites has become crucial for opinion mining, sentiment analysis, and identifying cyberbullies—online trolls
We propose a deep learning-based architecture for sarcasm detection, which leverages self-attention to enable the interpretability of the model while achieving state-of-the-art performance on various datasets
Summary
Sarcasm is a rhetorical way of expressing dislike or negative emotions using exaggerated language constructs. It is an assortment of mockery and false politeness to intensify hostility without explicitly doing so. Earlier works on sarcasm detection on texts use lexical (content) and pragmatic (context) cues [3] such as interjections, punctuation, and sentimental shifts, which are major indicators of sarcasm [4]. In these works, the features are hand-crafted and cannot generalize in the presence of informal language and figurative slang that is widely used in online conversations
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