Linguistic Features in Automatic Sarcasm Detection
Authors: Martina Ducret, Lauren Kruse, Carlos Martinez, Anna Feldman, Jing Peng (Montclair State University, 2020)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
We explore linguistic features that contribute to sarcasm detection. The linguistic features that we investigate are a combination of text and word complexity, stylistic and psychological features. We experiment with sarcastic tweets with and without context. The results of our experiments indicate that contextual information is crucial for sarcasm prediction. One important observation is that sarcastic tweets are typically incongruent with their context in terms of sentiment or emotional load.

Introduction
Sarcasm, or verbal irony, is a figurative language device employed to convey the opposite meaning of what is actually being said. % AUTHOR In verbal communication, a pause, intonation, or look can provide the cues necessary to determine whether there is sarcastic intent behind a comment. In writing, these social cues are inaccessible. Thus, we must rely on our understanding of the world, the speaker, and the context beyond the statement to discern between sarcasm and sincerity. This task has proven to be so subjective that social media users moderate their own comments using symbols and hashtags such as /s and \#sarcasm to denote the sentiment on Reddit and Twitter, respectively. In fact, the dataset used in this paper was collected using such hashtags AUTHOR.\let\thefootnote\relax For machines, the lack of real-word knowledge is detrimental to their understanding of sarcasm as it hinders many natural language processing applications. Beyond social-media conversations, assessing product reviews as positive or negative requires an understanding of both rhetorical and literary devices. Back in 2012, BIC rolled out a ``For Her” line of pens which led their intended female audience to poke fun at the misogynist message of the product. One reviewer commented, ``Well at last pens for us ladies to use\dots now all we need is ``for her"" paper and I can finally learn to write!”. While this review seems positive and gave the product four stars, our understanding of the social climate today leads us to conclude that this review is sarcastic and should be classified as such. In social media communication, new slang words are introduced every day and emojis are often used to negate the sentiment of the text. In addition, stylistic devices and stylometric features are also often employed to convey a meaning opposite from its literal interpretation. While deep learning models can be very effective in their detection of sarcasm, they provide a ``black box” approach that gives linguists little to no insight into what features are characteristic of sarcasm. %The goal of our work is to shed a light on these features and determine the strongest indicators of sarcasm in social media. The purpose of the current work is to learn linguistic patterns associated with sarcastic tweets and their contexts and determine which are the strongest indicators of sarcasm. The next step is to combine these observations with transformer-based architectures to achieve a better prediction accuracy.

Conclusion
In this paper we explored the role various linguistic features play in computational sarcasm detection. We investigated a combination of text and word complexity features, stylistic and psychological features. The result of our experiments indicate that contextual information is crucial for sarcasm detection. We also observed that sarcastic tweets are often incongruent with their context in terms of sentiment or emotional load. Using a Random Forest classifier and the features we extracted we obtain promising results. Our current work is concerned with combining these observations with transformer-based architectures to achieve a better prediction accuracy.