Review of Latent Dirichlet Allocation Methods Usable in Voice of Customer Analysis

Lucie Sperkova

Acta Informatica Pragensia, 2018, vol. 2018, issue 2, 152-165

Abstract: The aim of the article is to detect and review existing topic modelling methods of Latent Dirichlet Allocation and their modifications usable in Voice of Customer analysis. Voice of Customer is expressed mainly through textual comments which often focus on the evaluation of products or services the customer consumes. The most studied data source are customer reviews which contain next to the textual comments also ratings in form of scales. The aim of the topic models is to mine the topics and their aspects the customers are evaluating in their reviews and assign to them a particular sentiment or emotion. The author completed a systematic literature review of peer-reviewed published journal articles indexed in leading databases of Scopus and Web of Science and concerning the current use of Latent Dirichlet Allocation model variants in Voice of Customer textual analysis for performing the tasks of aspect detection, emotion detection, personality detection and sentiment assignation. In total, 38 modifications of the LDA model were identified with the reference to their first application in the research of text analytics. The review is intended for researchers in customer analytics the field of sentiment or emotion detection, and moreover as results from the review, for studies in personality recognition based on the textual data. The review offers a basic overview and comparison of LDA modifications which can be considered as a knowledge baseline for selection in a specific application. The scope of the literature examination is limited to the period of years 2003-2018 with the application relevant to the analysis of Voice of Customer subjective textual data only which is closely connected to the area of marketing or customer relationship management.

Keywords: Aspect detection; LDA; Sentiment; Text analytics; Topic models; VoC (search for similar items in EconPapers)
Date: 2018
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DOI: 10.18267/j.aip.120

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