Are Word Embedding-based Features Useful for Sarcasm Detection?
Table 2: Performance of unigrams versus our similarity-based features using embeddings from Word2Vec
['[BOLD] Features', '[BOLD] P', '[BOLD] R', '[BOLD] F']
The embedding in this case is Word2Vec. The four rows show baseline sets of features: unigrams, unweighted similarity using word embeddings (S), weighted similarity using word embeddings (WS) and both (i.e., unweighted plus weighted similarities using word embeddings). Using only unigrams as features gives a F-score of 72.53%, while only unweighted and weighted features gives F-score of 69.49% and 58.26% respectively. This validates our intuition that word embedding-based features alone are not sufficient, and should be augmented with other features.