Sentiment Analysis of Public Opinion on the Internet of Things (IoT) Through Social Media

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Khosravi Sangari Elieh; Khadivar Ameneh; Abbasi Fatemeh; Kabirian Mahdieh

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Journal Paper

Paper Information

Journal: INTERNATIONAL JOURNAL OF WEB RESEARCH Year:2026 | Volume:9 | Issue:1 Page(s): 85-96

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Information Journal Paper

Title

Sentiment Analysis of Public Opinion on the Internet of Things (IoT) Through Social Media

Author(s)

Khosravi Sangari Elieh | Khadivar Ameneh | Abbasi Fatemeh | Kabirian Mahdieh | Issue Writer Certificate

Keywords

Sentiment Analysis

Social Media Analytics

Topic Modeling

Internet of Things (IoT)

Abstract

Social media offers a timely lens into public perceptions of emerging technologies. To assess public opinion on the Internet of Things (IoT), we analyzed a corpus of 824,845 IoT-related posts collected from X between 2013 and 2022. Using Latent Dirichlet Allocation (LDA), we identified seven primary themes of discussion: Smart Home, Business Intelligence, Artificial Intelligence, Smart City, IoT Usage, Emerging Technologies, and Blockchain. We then applied an unsupervised machine-learning technique to evaluate sentiment toward each theme. Overall, public discourse was positive: 46.78% of tweets expressed positive sentiment, 43.41% were neutral, and 9.81% were negative. Although predictable, short-term shifts in tone occurred around specific events, interest in these themes remained consistent throughout the study period. These findings suggest that the Internet of Things is generally perceived favorably and demonstrate how large-scale social media analytics can capture authentic, real-time attitudes toward complex technologies. By linking public opinion to specific topics of discussion, our results provide valuable insights for researchers, policymakers, and product teams seeking to align IoT development with societal expectations.

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