Using Games with a Purpose and Bootstrapping to Create Domain-Specific Sentiment Lexicons


Weichselbraun, Albert, Gindl, Stefan and Scharl, Arno. (2011). Using Games with a Purpose and Bootstrapping to Create Domain-Specific Sentiment Lexicons. Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM 2011), Glasgow, Scotland


Sentiment detection analyzes the positive or negative polarity of text. The field has received considerable attention in recent years, since it plays an important role in providing means to assess user opinions regarding an organisation's products, services, or actions. Approaches towards sentiment detection include machine learning techniques as well as computationally less expensive methods. The latter rely on the use of language-specific sentiment lexicons, which are lists of sentiment terms with their corresponding sentiment value. The effort involved in creating, customizing, and extending sentiment lexicons is considerable, particularly if less common languages and domains are targeted without access to appropriate language resources. This paper proposes a semi-automatic approach for the creation of sentiment lexicons which assigns sentiment values to sentiment terms via crowdsourcing. Furthermore, it introduces a bootstrapping process operating on unlabeled domain documents to extend the created lexicons, and to customize them according to the particular use case. This process considers sentiment terms as well as sentiment indicators occurring in the discourse surrounding a particular topic. Such indicators are associated with a positive or negative context in a particular domain, but might have a neutral connotation in other domains. A formal evaluation shows that bootstrapping considerably improves the method's recall. Automatically created lexicons yield a performance comparable to professionally created language resources such as the General Inquirer.

Keywords: Sentiment Detection, Sentiment Analysis, Bootstrapping, Language Resources, Sentiment Lexicon, Crowd-Sourcing

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