Are Word Embedding-based Features Useful for Sarcasm Detection?
Table 3: Performance obtained on augmenting word embedding features to features from four prior works, for four word embeddings; L: Liebrecht et al. (2013), G: González-Ibánez et al. (2011a), B: Buschmeier et al. (2014) , J: Joshi et al. (2015)
['[EMPTY]', '[BOLD] LSA P', '[BOLD] LSA R', '[BOLD] LSA F', '[BOLD] GloVe P', '[BOLD] GloVe R', '[BOLD] GloVe F', '[BOLD] Dependency Weights P', '[BOLD] Dependency Weights R', '[BOLD] Dependency Weights F', '[BOLD] Word2Vec P', '[BOLD] Word2Vec R', '[BOLD] Word2Vec F']
All entries in the tables are higher than the simple unigrams baseline, i.e., F-score for each of the four is higher than unigrams - highlighting that these are better features for sarcasm detection than simple unigrams. Values in bold indicate the best F-score for a given prior work-embedding type combination. In case of \newciteliebrecht for Word2Vec, the overall improvement in F-score is 4%. Precision increases by 8% while recall remains nearly unchanged. For features given in \newcitegonzalez, there is a negligible degradation of 0.91% when word embedding-based features based on Word2Vec are used. For \newcitebuschmeier for Word2Vec, we observe an improvement in F-score from 76.61% to 78.09%. Precision remains nearly unchanged while recall increases. In case of \newcite22 and Word2Vec, we observe a slight improvement of 0.20% when unweighted (S) features are used. This shows that word embedding-based features are useful, across four past works for Word2Vec. The maximum improvement is observed in case of \newciteliebrecht. It is around 4% in case of LSA, 5% in case of GloVe, 6% in case of Dependency weight-based and 4% in case of Word2Vec. These improvements are not directly comparable because the four embeddings have different vocabularies (since they are trained on different datasets) and vocabulary sizes, their results cannot be directly compared.