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 Machines 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 Wash Tee 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.