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

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