Rule-based Opinion Target and Aspect Extraction to Acquire Affective Knowledge


Gindl, Stefan, Weichselbraun, Albert and Scharl, Arno. (2013). Rule-based Opinion Target and Aspect Extraction to Acquire Affective Knowledge. First WWW Workshop on Multidisciplinary Approaches to Big Social Data Analysis (MABSDA 2013), Rio de Janeiro, Brazil


Opinion holder and opinion target extraction are among the most popular and challenging problems tackled by opinion mining researchers, recognizing the significant business value of such components and their importance for applications such as media monitoring and Web intelligence. This paper describes an approach that combines opinion target extraction with aspect extraction using syntactic patterns. It expands previous work limited by sentence boundaries and includes a heuristic for anaphora resolution to identify targets across sentences. Furthermore, it demonstrates the application of concepts known from research on open information extraction to the identification of relevant opinion aspects. Qualitative analyses performed on a corpus of 100\,000 Amazon product reviews show that the approach is promising. The extracted opinion targets and aspects are useful for enriching common knowledge resources and opinion mining ontologies, and support practitioners and researchers to identify opinions in document collections.

Keywords: Opinion mining, opinion target extraction, opinion aspect extraction

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