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        <title>Domain-Specific Sentiment Lexicons Induced from Labeled Documents</title>
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        <namePart type="given">Mazharul</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Domain-Specific Sentiment Lexicons Induced from Labeled Documents
%A Islam, SM Mazharul
%A Dong, Xin
%A de Melo, Gerard
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F islam-etal-2020-domain
%X 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.
%R 10.18653/v1/2020.coling-main.578
%U https://aclanthology.org/2020.coling-main.578
%U https://doi.org/10.18653/v1/2020.coling-main.578
%P 6576-6587

Markdown (Informal)

[Domain-Specific Sentiment Lexicons Induced from Labeled Documents](https://aclanthology.org/2020.coling-main.578) (Islam et al., COLING 2020)

• Domain-Specific Sentiment Lexicons Induced from Labeled Documents (Islam et al., COLING 2020)

ACL

• SM Mazharul Islam, Xin Dong, and Gerard de Melo. 2020. Domain-Specific Sentiment Lexicons Induced from Labeled Documents. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6576–6587, Barcelona, Spain (Online). International Committee on Computational Linguistics.