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Introduction
In recent years, consumers’ willingness to share consumption experiences online, coupled with the technology to analyze “big data”, offer marketing managers an unprecedented opportunity to collect market intelligence (Erevelles et al., 2016). Through online sentiment analysis, hereafter referred to as sentiment analysis, researchers can systematically extract and classify consumer emotions about products and services expressed in social network discussions and online postings to track brand attitudes and emerging market trends. While sentiment analysis presents tremendous opportunities to interpret a large body of data collected in a naturalistic setting, concerns have been expressed about the technique’s accuracy and practicality (Gonçalves et al., 2013). Moreover, apprehensions over online data volume, fragmented data sources, content bias and user exploitation have exposed the technique to critical scrutiny.
In light of these challenges, it is somewhat surprising that researchers have not devoted more attention to evaluating the overall feasibility of using sentiment analysis as a tool for online marketing research. Our study serves to fill this gap by reviewing the literature on the application of sentiment analysis in the marketing discipline. The review is specific to the literature published in scholarly peer-reviewed marketing journals between 2008 and 2016, which coincides with the technique’s general usage within the marketing discipline.
The current study makes two unique contributions to the field of marketing research in an interactive environment. First, it is one of the few papers to review the application of sentiment analysis in marketing research comprehensively. Second, the paper focuses attention on the limitations surrounding the utilization of this technique for marketing research and provides suggestions for more effective use.
Overview of sentiment analysis
While reviewing the literature, it is apparent that a misunderstanding often exists about what constitutes sentiment analysis. To provide conceptual clarity, sentiment analysis first needs to be distinguished from the broader literature on online text mining. With text-mining applications, researchers structure a large body of data from various online sources into numerous topics or themes which emerge from the body of textual data. In this regard, text mining is similar to traditional content analysis, since it allows researchers to efficiently extract, classify and manage a large body of data to identify hidden patterns or trends (He et al., 2013).
In contrast, sentiment analysis...