Online sentiment analysis in marketing research: a review

Abstract

Purpose The explosion of internet-generated content, coupled with methodologies such as sentiment analysis, present exciting opportunities for marketers to generate market intelligence on consumer attitudes and brand opinions. The purpose of this paper is to review the marketing literature on online sentiment analysis and examines the application of sentiment analysis from three main perspectives: the unit of analysis, sampling design and methods used in sentiment detection and statistical analysis. Design/methodology/approach The paper reviews the prior literature on the application of online sentiment analysis published in marketing journals over the period 2008-2016. Findings The findings highlight the uniqueness of online sentiment analysis in action-oriented marketing research and examine the technical, practical and ethical challenges faced by researchers. Practical implications The paper discusses the application of sentiment analysis in marketing research and offers recommendations to address the challenges researchers confront in using this technique. Originality/value This study provides academics and practitioners with a comprehensive review of the application of online sentiment analysis within the marketing discipline. The paper focuses attention on the limitations surrounding the utilization of this technique and provides suggestions for mitigating these challenges.

Leveraging Artificial Intelligence in Business: Implications, Applications and Methods

“…As we focused on papers dealing with both AI and Business, this topic focuses on how to implement general-use algorithms for business implementation. The most prominent usage scenarios we found in our corpus included: sales forecasting that enable the assessment of sufficiency of companies' plans (Castillo et al 2017), sentiment analysis and opinion mining to extract subjective information out of consumer-generated comments (Giatsoglou et al 2017;Rambocas and Pacheco 2018), and risk evaluation (Tsai 2014 ;Zhang et al 2010). Moreover, Prediction Models could be exploited in several industries as well, as recent studies suggest in the medical field for instance to prevent and forecast epidemics.…”

All work and no play: A text analysis

“…We noted earlier that sentiment analysis is a feature of text analysis that many authors have refererred to—for example, Gunter, Koteyko, and Atanasova (2014); Rambocas and Pacheco (2018); and Keramatfar and Amirkhani (2018). There have been studies relating sentiment analysis of text to scalar responses, which we discuss later—and some authors have looked at whether there are differences between genders in the output (Thelwall, 2018).…”

“…Subject matter includes health (Carah, Meurk, & Angus, 2015; Cunningham, Tablan, Roberts, & Bontcheva, 2013; Khoo & Johnkhan, 2017; Topaz et al, 2016), politics (Ceron, Curini, & Iacus, 2014; Light, 2014; Parackal, Mather, & Holdsworth, 2018), gaming (Zagal, Tomuro, & Shepitsen, 2011), and organizational culture (Pandey & Pandey, 2017). Two recent reviews (Keramatfar & Amirkhani, 2018; Rambocas & Pacheco, 2018) of the subject categories to which sentiment analysis in particular have been applied, both indicate that computing and IT journals are the dominant source. Rambocas and Pacheco (2018) note that marketing journals are in third place, following computing and communications.…”

“…Two recent reviews (Keramatfar & Amirkhani, 2018; Rambocas & Pacheco, 2018) of the subject categories to which sentiment analysis in particular have been applied, both indicate that computing and IT journals are the dominant source. Rambocas and Pacheco (2018) note that marketing journals are in third place, following computing and communications.…”

Sentiment analysis of social media content in Croatian hotel industry

“…With the spread of UGC on various platforms, different opportunities have arisen for marketers and researchers. Through online sentiment analysis, the "big data" produced offered marketers the opportunity to gather marketing intelligence (Erevelles, Fukawa and Swayne, 2016) and researchers the ability to systematically extract and classify consumer sentiment about products and services expressed in comments and postings on social media sites in order to obtain brand attitudes and new market trends (Rambocas and Pacheco, 2018). User-generated content with a focus on comments/reviews in social media can significantly influence the buying decision of potential customers (Ma, Cheng and Hsiao, 2018).…”