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