Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology

被引:479
作者
Maier, Daniel [1 ]
Waldherr, A. [2 ]
Miltner, P. [1 ]
Wiedemann, G. [3 ]
Niekler, A. [3 ]
Keinert, A. [1 ]
Pfetsch, B. [1 ]
Heyer, G. [3 ]
Reber, U. [4 ]
Haeussler, T. [4 ]
Schmid-Petri, H. [5 ]
Adam, S. [4 ]
机构
[1] Free Univ Berlin, Inst Media & Commun Studies, Berlin, Germany
[2] Univ Munster, Dept Commun, Munster, Germany
[3] Univ Leipzig, Comp Sci Inst, Leipzig, Germany
[4] Univ Bern, Inst Commun & Media Studies, Bern, Switzerland
[5] Univ Passau, Passau, Germany
基金
瑞士国家科学基金会;
关键词
TEXT;
D O I
10.1080/19312458.2018.1430754
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication research. Yet, questions regarding reliability and validity of the approach have received little attention thus far. In applying LDA to textual data, researchers need to tackle at least four major challenges that affect these criteria: (a) appropriate pre-processing of the text collection; (b) adequate selection of model parameters, including the number of topics to be generated; (c) evaluation of the model's reliability; and (d) the process of validly interpreting the resulting topics. We review the research literature dealing with these questions and propose a methodology that approaches these challenges. Our overall goal is to make LDA topic modeling more accessible to communication researchers and to ensure compliance with disciplinary standards. Consequently, we develop a brief hands-on user guide for applying LDA topic modeling. We demonstrate the value of our approach with empirical data from an ongoing research project.
引用
收藏
页码:93 / 118
页数:26
相关论文
共 68 条
  • [1] [Anonymous], 2002, MALLET: A machine learning for language toolkit
  • [2] [Anonymous], 2007, HDB LATENT SEMANTIC
  • [3] [Anonymous], 2011, Mining of Massive Datasets
  • [4] [Anonymous], TEXT THEMA STUDIEN T
  • [5] [Anonymous], 2017, CONTENT ANAL GUIDEBO
  • [6] [Anonymous], 2009, INTRO INFORM RETRIEV
  • [7] Recognising speakers from the topics they talk about
    Baum, Doris
    [J]. SPEECH COMMUNICATION, 2012, 54 (10) : 1132 - 1142
  • [8] Biber D., 1993, Literary Linguist. Comput., V8, P243, DOI DOI 10.1093/LLC/8.4.243
  • [9] Mining Crowdsourced First Impressions in Online Social Video
    Biel, Joan-Isaac
    Gatica-Perez, Daniel
    [J]. IEEE TRANSACTIONS ON MULTIMEDIA, 2014, 16 (07) : 2062 - 2074
  • [10] Blei D. M., 2006, INT C MACH LEARN PIT