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首页> 外文期刊>Journal of the American Society for Information Science and Technology >Extracting Speaker-Specific Functional Expressions From Political Speeches Using Random Forests in Order to Investigate Speakers' Political Styles
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Extracting Speaker-Specific Functional Expressions From Political Speeches Using Random Forests in Order to Investigate Speakers' Political Styles

机译：使用随机森林从政治演讲中提取演讲者特定的功能表达，以调查演讲者的政治风格

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In this study we extracted speaker-specific functional expressions from political speeches using random forests to investigate speakers' political styles. Along with methodological development, stylistics has expanded its scope into new areas of application such as authorship profiling and sentiment analysis in addition to conventional areas such as authorship attribution and genre-based text classification. Among these, computational sociolinguistics, which aims at providing a systematic and solid basis for sociolinguistic analysis using machine learning and linguistically-motivated features, is a potentially important area. In this study we showed the effectiveness of the random forests classifier for such tasks by applying it to Japanese prime ministers' Diet speeches.The results demonstrated that our method successfully extracted the speaker-specific expressions of two Japanese prime ministers, and enabled us to investigate their political styles in a systematic manner. The method can be applied to sociolinguistic analysis of various other types of texts, and in this way, this study will contribute to developing the area of computational sociolinguistics.
机译：在这项研究中，我们使用随机森林从演讲者中提取演讲者特定的功能表达，以调查演讲者的政治风格。随着方法论的发展，除了传统领域（如作者身份归因和基于体裁的文本分类）外，文体学的范围也扩大到了新的应用领域，例如作者身份分析和情感分析。其中，计算社会语言学旨在为使用机器学习和语言动机特征的社会语言分析提供系统而坚实的基础，这是一个潜在的重要领域。在这项研究中，我们通过将随机森林分类器应用于日本首相的Diet演讲中来证明其有效性，结果表明我们的方法成功地提取了两位日本首相的说话人特定表达，并使我们能够进行调查他们的政治风格是系统的。该方法可用于各种其他类型的文本的社会语言分析，因此，本研究将有助于发展计算社会语言学领域。

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