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随着互联网和 NLP 的迅猛发展，如何从大量的文本信息中挖掘有价值的信息成为一个热点话题。案例引入文本挖掘方法中的 LDA 和 DTM 模型，并应用于经过风险关键词筛选后的年报风险信息，分别探究了2001-2012、 2013-2016、 2017-2020 以及2021-2023 这四个时间段的金融风险类别及风险点的结果，并进一步分析其演化趋势。

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随着互联网和 NLP 的迅猛发展，如何从大量的文本信息中挖掘有价值的信息成为一个热点话题。案例引入文本挖掘方法中的 LDA 和 DTM 模型，并应用于经过风险关键词筛选后的年报风险信息，分别探究了2001-2012、 2013-2016、 2017-2020 以及2021-2023 这四个时间段的金融风险类别及风险点的结果，并进一步分析其演化趋势。

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中国经管实验教学案例平台

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