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• W2772531999 abstract "Sentiment analysis is the computational task of extracting sentiment from a text document – for example whether it expresses a positive, negative or neutral opinion. Various approaches have been introduced in recent years, using a range of different techniques to extract sentiment information from a document. Measuring these methods against a gold standard dataset is a useful way to evaluate such systems. However, different sentiment analysis techniques represent sentiment values in different ways, such as discrete categorical classes or continuous numerical sentiment scores. This creates a challenge for evaluating and comparing such systems; in particular assessing numerical scores against datasets that use fixed classes is difficult, because the numerical outputs have to be mapped onto the ordered classes. This paper proposes a novel calibration technique that uses precision vs. recall curves to set class thresholds to optimize a continuous sentiment analyser’s performance against a discrete gold standard dataset. In experiments mapping a continuous score onto a three-class classification of movie reviews, we show that calibration results in a substantial increase in f-score when compared to a non-calibrated mapping." @default.
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• W2772531999 date "2017-11-10" @default.
• W2772531999 modified "2023-09-25" @default.
• W2772531999 title "A Calibration Method for the Evaluation of Sentiment Analysis" @default.
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• W2772531999 doi "https://doi.org/10.26615/978-954-452-049-6_084" @default.
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