`Who would have thought of that!': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection

TL;DR

This paper introduces a hierarchical topic model designed to identify sarcasm-prevalent topics and detect sarcasm in tweets by analyzing word sentiment mixtures, outperforming previous classifiers by 25%.

Contribution

It presents the first simple topic model specifically for sarcasm detection, leveraging sentiment mixtures within topics to improve accuracy.

Findings

Identifies sarcasm-prevalent topics like 'work' and 'weather'

Discovers sentiment mixtures in sarcastic texts

Achieves 25% better sarcasm detection accuracy than prior classifiers

Abstract

Topic Models have been reported to be beneficial for aspect-based sentiment analysis. This paper reports a simple topic model for sarcasm detection, a first, to the best of our knowledge. Designed on the basis of the intuition that sarcastic tweets are likely to have a mixture of words of both sentiments as against tweets with literal sentiment (either positive or negative), our hierarchical topic model discovers sarcasm-prevalent topics and topic-level sentiment. Using a dataset of tweets labeled using hashtags, the model estimates topic-level, and sentiment-level distributions. Our evaluation shows that topics such as `work', `gun laws', `weather' are sarcasm-prevalent topics. Our model is also able to discover the mixture of sentiment-bearing words that exist in a text of a given sentiment-related label. Finally, we apply our model to predict sarcasm in tweets. We outperform two prior work based on statistical classifiers with specific features, by around 25\%.