A Unified Feature Representation for Lexical Connotations
Table 7: Cluster connotation purity ratios. Emo is based on no emotions (0) or any number (≥1).
['[BOLD] Aspect   [ITALIC]  [BOLD] a', '[ITALIC]  [BOLD] c', '[ITALIC]  [BOLD] r [BOLD] a [BOLD] ( [ITALIC]  [BOLD] C [BOLD] ) [ITALIC]  [BOLD] c', '[ITALIC]  [BOLD] r [BOLD] a [BOLD] ( [ITALIC]  [BOLD] P [BOLD] ) [ITALIC]  [BOLD] c']
We find that across connotation aspects, these ratios are higher for the learned connotation embeddings, compared to pre-trained word embeddings. This shows the connotation embedding model clusters words based more on connotation labels than pre-trained word embeddings.