Identifying Films with Noir Characteristics Using Audience's Tags on MovieLens
We consider the noir classification problem by exploring noir attributes and what films are likely to be regarded as noirish from the perspective of a wide Internet audience. We use a dataset consisting of more than 30,000 films with relevant tags added by users of MovieLens, a web-based recommendation system. Based on this data, we develop a statistical model to identify films with noir characteristics using these free-form tags. After retrieving information for describing films from tags, we implement a one-class nearest neighbors algorithm to recognize noirish films by learning from IMDb-labeled noirs. Our analysis evidences film noirs' close relationship with German Expressionism, French Poetic Realism, British thrillers, and American pre-code crime pictures, revealing the similarities and differences between neo noirs after 1960 and noirs in the classic period.
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