主题建模的简单Python脚本

K. Musunuru
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引用次数: 0

摘要

在文献验证和题目评价中,主观评价普遍存在。虽然主观评估是一种有效的做法,但在结果的有效性方面造成了空白。直觉是独特的,往往依赖于其他几个方面,而不是方法论。因此,在行动中可能会出现不合理但不公平的个人意见。自然语言处理(NLP)提供了健壮的机制或技术来评估非结构化数据。潜狄利克雷分配(Latent Dirichlet Allocation, LDA)是在处理非结构化的主观数据时加入逻辑的一种方法。本文解释了topmodpy使用潜在狄利克雷分配(Latent Dirichlet Allocation, LDA)执行潜在语义分析(LSA)的适用性。topmopy是一个Python脚本,是12个不同函数的集合,每个函数都有一个独特的目标。本文展示了如何对使用有效搜索条件收集的某些数据使用topmopy模块。Topmodpy模块发现获得了这些潜在构念相关的搜索条件。从而证明了topmodpy模块的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Topmodpy: A Simple Python Script for Topic Modeling
Subjective assessment is rampant in literature verification and title evaluation. While subjective assessment is a valid practice but creates a void in terms of validity of results. Intuition is unique and tend to depend on several other aspects which are not methodological. As a result of which, there may be a possibility of unreasonable yet unfair amount of personal opinion in action. Natural Language Processing (NLP) offers robust mechanisms or techniques to evaluate unstructured data. Latent Dirichlet Allocation (LDA) is one of such techniques which adds logic while processing unstructured but subjective data. This article explains suitability of topmodpy to perform Latent Semantic Analysis (LSA) using Latent Dirichlet Allocation (LDA). topmopy is a Python script and is a collection of 12 different functions each with a unique aim. This article shows as how to use topmopy module on certain data collected using a valid search criteria. topmodpy module found to have obtained these latent constructs related to search criteria. Hence the efficacy of the topmodpy module has been proved.
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