The search for topics related to electric mobility: a comparative analysis of some of the most widely used methods in the literature

被引：2
作者：
Alboni, Fabrizio [1 ]
Pavone, Pasquale [1 ,2 ]
Russo, Margherita [1 ,2 ]
机构：
[1] Univ Modena & Reggio Emilia, Dept Econ, Modena, Italy
[2] CAPP, Modena, Italy
来源：
METRON-INTERNATIONAL JOURNAL OF STATISTICS | 2023年 / 81卷 / 03期
关键词：
Topic detection; Text mining; Cramer's V; Coherence indexes; Semantic similarities; Electric mobility; NUMBER;
D O I：
10.1007/s40300-023-00255-2
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Identifying the topics addressed in a corpus is one of the primary concerns of automated text analysis. This paper aims to contribute to the comparative analysis of various methodologies. Specifically, a comparison is made of the results obtained by applying the most prevalent topic identification techniques to the same corpus. The analysis is conducted on a large database of original text created from an e-mobility newsletter. To evaluate the outcomes of the methodologies, two criteria are used. First, the semantic coherence and similarities of the various methods are assessed. The second step involves processing the degree of association between the topics identified by the various models.
引用
收藏
页码：367 / 391
页数：25
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