CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
Figure 3: Comparison with variants of the proposed CASCADE network. All combinations use content-based CNN.
['CASCADE', 'CASCADE', 'CASCADE', 'CASCADE', 'Main', 'Main', 'Main', 'Main', 'Pol', 'Pol']
We experiment on multiple variants of CASCADE so as to analyze the importance of the various features present in its architecture. First, we test performance for the content-based CNN only (row 1). This setting provides the worst relative performance with almost 10% lesser accuracy than optimal. Next, we include contextual features to this network. Here, the effect of discourse features is primarily seen in the Pol dataset getting an increase of 3% in F1 (row 2). A major boost in performance is observed (8−12% accuracy and F1) when user embeddings are introduced (row 5). Overall, CASCADE consisting of CNN with user embeddings and contextual discourse features provide the best performance in all three datasets (row 6). We investigate the learnt user embeddings in more detail. The users who have greater sarcastic comments (atleast 2 more than the other type) are termed as sarcastic users (colored red). Conversely, the users having lesser sarcastic comments are called non-sarcastic users (colored green). Equal number of users from both the categories are plotted. However, the sarcastic users have a greater spread than the non-sarcastic ones (red belt around the green region). This is also evident from the variances of the distributions where the sarcastic distribution comprises of 10.92 variance as opposed to 5.20 variance of the non-sarcastic distribution. We can infer from this observation that the user embeddings belonging to this non-overlapping red-region provide discriminative information regarding the sarcastic tendencies of their users.