Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces

Understand

Recent approaches for sentiment lexicon induction have capitalized on pre-trained word embeddings that capture latent semantic properties.

• However, embeddings obtained by optimizing performance of a given task (e.g.
• predicting contextual words) are sub-optimal for other applications.
• In this paper, we address this problem by exploiting task-specific representations, induced via embedding sub-space projection.