Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces

TL;DR

This paper introduces a method to expand subjective lexicons for social media analysis by using task-specific embedding subspaces, improving lexicon quality and sentiment classification performance, especially with limited training data.

Contribution

It proposes a novel approach that jointly learns semantic representations and predictors for multiple properties using embedding subspaces, enhancing lexicon expansion and sentiment analysis.

Findings

Outperforms previous lexicon expansion methods.

Effective even with limited training data.

Lexicon-based classifiers achieve competitive performance.

Abstract

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. This allows us to expand lexicons describing multiple semantic properties. For each property, our model jointly learns suitable representations and the concomitant predictor. Experiments conducted over multiple subjective lexicons, show that our model outperforms previous work and other baselines; even in low training data regimes. Furthermore, lexicon-based sentiment classifiers built on top of our lexicons outperform similar resources and yield performances comparable to those of supervised models.