Sarcasm Detection | A classification problem

This is the first part of series of summary of various research papers on sarcasm detection.

I do not post any links to the papers because whatever I summarize is full and sufficient to get started.

Paper 1

Below are few lines from the paper

“semantic similarity, emoticons, counterfactuality, etc. introduce features related to ambiguity, unexpectedness, emotional scenario”

“include seven sets of features such as maximum/minimum/gap of intensity of adjectives and adverbs, max/min/average number of synonyms and synsets for words in the target text, etc.”


Problem: say chinese homophonic sentences

DL approaches

Word embeddings, CNN, LSTM, recursive SVM, Deep Convolutional Network.

Future enhancements

ISSUES: Manual or distant supervised datasets, skewed data in labeled datasets, sarcasm and irony.