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.
Highlights
In computational linguistics and natural language processing (NLP), sentiment analysis involves extracting emotion and opinion from text data
In the deep learning community, in the NLP domain, it is common to scale up the number of parameters in successful models to eke out additional performance gains
With the limited size of our training set, we needed alternative techniques to increase the performance of our models
Summary
In computational linguistics and natural language processing (NLP), sentiment analysis involves extracting emotion and opinion from text data. With the modern volume of text data—which has long rendered human annotation infeasible— automated sentiment analysis is used, for example, by businesses in evaluating customer feedback to make informed decisions regarding product development and risk management (Turney, 2002; Cabral and Hortacsu, 2010). Sentiment analysis has been used with the intent to improve consumer experience through aggregated and curated feedback from other consumers, in retail (Kumar and Lee, 2006; Tang et al, 2009; Yu et al, 2013), e-commerce (Bhatt et al, 2015; Haque et al, 2018), and entertainment (Terveen et al, 1997; Pang et al, 2002).
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