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Register a new userRollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual Data
Rollinglda: خوارزمية تحديث من مخصصات Dirichlet الكامنة للبناء سلسلة زمنية ثابتة من البيانات النصية
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نقترح نسخ المتداول من تخصيص Dirichlet الكامن، يسمى Rollinglda. من خلال نهج متتابع، فإنه يتيح بناء سلسلة الزمن القائم على LDA من الموضوعات التي تتفق مع الدول السابقة لنماذج LDA. بعد النمذجة الأولي، يمكن حساب التحديثات بكفاءة، مما يسمح للرصد في الوقت الفعلي والكشف عن الأحداث أو الاستراتيجات الهيكلية. لهذا الغرض، نقترح تدابير تشابه مناسبة للموضوعات وتوفير دليل محاكاة على التفوق على النهج الأخرى الشائعة الاستخدام. يتم توضيح كفاية الطريقة الناتجة من خلال تطبيق على مثال Corpus. على وجه الخصوص، نحسب التشابه المتمثل في توزيعات الموضوعات التي تم الحصول عليها بالتتابع على فترات زمنية متتالية. للحصول على مثال تمثيلي، تتكون من مقالات نيويورك تايمز من عام 1980 إلى 2020، نقوم بتحليل تأثير العديد من خيارات المعلمات ضبطها وندير طريقة Rollinglda على مجموعة البيانات الكاملة التي تبلغ حوالي 4 ملايين مادة لإظهار جدوائها.
We propose a rolling version of the Latent Dirichlet Allocation, called RollingLDA. By a sequential approach, it enables the construction of LDA-based time series of topics that are consistent with previous states of LDA models. After an initial modeling, updates can be computed efficiently, allowing for real-time monitoring and detection of events or structural breaks. For this purpose, we propose suitable similarity measures for topics and provide simulation evidence of superiority over other commonly used approaches. The adequacy of the resulting method is illustrated by an application to an example corpus. In particular, we compute the similarity of sequentially obtained topic and word distributions over consecutive time periods. For a representative example corpus consisting of The New York Times articles from 1980 to 2020, we analyze the effect of several tuning parameter choices and we run the RollingLDA method on the full dataset of approximately 4 million articles to demonstrate its feasibility.
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Latent Dirichlet allocation (LDA), a widely used topic model, is often employed as a fundamental tool for text analysis in various applications. However, the training process of the LDA model typically requires massive text corpus data. On one hand,
such massive data may expose private information in the training data, thereby incurring significant privacy concerns. On the other hand, the efficiency of the LDA model training may be impacted, since LDA training often needs to handle these massive text corpus data. To address the privacy issues in LDA model training, some recent works have combined LDA training algorithms that are based on collapsed Gibbs sampling (CGS) with differential privacy. Nevertheless, these works usually have a high accumulative privacy budget due to vast iterations in CGS. Moreover, these works always have low efficiency due to handling massive text corpus data. To improve the privacy guarantee and efficiency, we combine a subsampling method with CGS and propose a novel LDA training algorithm with differential privacy, SUB-LDA. We find that subsampling in CGS naturally improves efficiency while amplifying privacy. We propose a novel metric, the efficiency--privacy function, to evaluate improvements of the privacy guarantee and efficiency. Based on a conventional subsampling method, we propose an adaptive subsampling method to improve the model's utility produced by SUB-LDA when the subsampling ratio is small. We provide a comprehensive analysis of SUB-LDA, and the experiment results validate its efficiency and privacy guarantee improvements.
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