Augmenting semantic lexicons using word embeddings and transfer learning

TL;DR

This paper introduces two models for automatically expanding sentiment lexicons using word embeddings and transfer learning, improving efficiency and accuracy over manual methods.

Contribution

It proposes a deep Transformer-based model leveraging word definitions for better lexicon expansion, advancing the state of lexicon-based sentiment analysis.

Findings

Both models match Amazon Mechanical Turk accuracy

Deep Transformer model outperforms baseline

Cost-effective lexicon expansion method

Abstract

Sentiment-aware intelligent systems are essential to a wide array of applications. These systems are driven by language models which broadly fall into two paradigms: Lexicon-based and contextual. Although recent contextual models are increasingly dominant, we still see demand for lexicon-based models because of their interpretability and ease of use. For example, lexicon-based models allow researchers to readily determine which words and phrases contribute most to a change in measured sentiment. A challenge for any lexicon-based approach is that the lexicon needs to be routinely expanded with new words and expressions. Here, we propose two models for automatic lexicon expansion. Our first model establishes a baseline employing a simple and shallow neural network initialized with pre-trained word embeddings using a non-contextual approach. Our second model improves upon our baseline, featuring a deep Transformer-based network that brings to bear word definitions to estimate their lexical polarity. Our evaluation shows that both models are able to score new words with a similar accuracy to reviewers from Amazon Mechanical Turk, but at a fraction of the cost.