Abstract

Sentiment analysis is an area of substantial relevance both in industry and in academia, including for instance in social studies. Although supervised learning algorithms have advanced considerably in recent years, in many settings it remains more practical to apply an unsupervised technique. The latter are oftentimes based on sentiment lexicons. However, existing sentiment lexicons reflect an abstract notion of polarity and do not do justice to the substantial differences of word polarities between different domains. In this work, we draw on a collection of domain-specific data to induce a set of 24 domain-specific sentiment lexicons. We rely on initial linear models to induce initial word intensity scores, and then train new deep models based on word vector representations to overcome the scarcity of the original seed data. Our analysis shows substantial differences between domains, which make domain-specific sentiment lexicons a promising form of lexical resource in downstream tasks, and the predicted lexicons indeed perform effectively on tasks such as review classification and cross-lingual word sentiment prediction.

Highlights

  • Sentiment analysis is among the most prominent forms of natural language processing, with applications such as social media analytics (Rosenthal et al, 2017; Wang et al, 2019; Shoeb et al, 2019), marketing and customer support (Gamon, 2004), as well as recommendation (Yang et al, 2013)

  • Others offer more informative intensity scores to account for the fact that some words are more negative or positive than others

  • An emphatic word such as spectacular is generally considered stronger than a simple good. Such scores could be in the range [−1, 1], with −1 denoting the most negative sentiment polarity, whereas +1 is the most positive score

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Summary

Introduction

Sentiment analysis is among the most prominent forms of natural language processing, with applications such as social media analytics (Rosenthal et al, 2017; Wang et al, 2019; Shoeb et al, 2019), marketing and customer support (Gamon, 2004), as well as recommendation (Yang et al, 2013). There are numerous techniques for lexicon-driven sentiment analysis (Taboada et al, 2011), SentiStrength (Thelwall et al, 2010) being an example of a more modern lexicon-driven sentiment analysis system. A sentiment lexicon is a resource that, for a given word (form) w, provides an annotation label lw describing its overall sentiment polarity. An emphatic word such as spectacular is generally considered stronger than a simple good (de Melo and Bansal, 2013). Such scores could be in the range [−1, 1], with −1 denoting the most negative sentiment polarity, whereas +1 is the most positive score

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