Public Sentiment Analysis on the Implementation of Digital Technologies in Business Strategies
Year: 2026
Type: article
Abstract: This paper reviews how public sentiment analysis has been used to understand the implementation of digital technologies in business strategies, and what the accumulated evidence reveals about the public reception of artificial intelligence chatbots, e-commerce platforms, digital payment systems and social commerce. Because primary data collection lies beyond the scope of this study, the paper adopts a systematic literature review design, applying a documented search, screening and inclusion protocol to published studies and synthesising only findings that the original studies themselves reported. Thirty-five verified works constitute the corpus, including foundational treatments of opinion mining, methodological surveys, and applied studies of brand, service and platform sentiment. The review traces three methodological generations, from lexicon-based scoring through supervised machine learning to deep learning and transformer models, and shows that each generation widened the scale of public text that firms can monitor while leaving persistent problems of sarcasm, context and language coverage unresolved. Across application domains, the published evidence indicates that public sentiment responds less to the mere presence of a technology than to the quality of the experience it delivers, that negative sentiment is weighted more heavily than positive sentiment in market outcomes, and that sentiment about service automation turns sharply negative when automated agents fail or when customers feel deceived about the identity of their counterpart. The paper organises these findings into sector patterns, translates them into principles for using sentiment as strategic feedback, and sets out the limitations of the evidence, including platform bias, automated accounts and the scarcity of Indonesian language resources. It closes with a research agenda for Indonesian business scholarship, where public data are abundant but verified local sentiment resources remain thin.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225650112
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