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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Business Management and Islamic Banking Nusantara Journal of Artificial Intelligence and Information Systems
Zia Ul Rehman Zafar
Universitas Muhammadiyah Surakarta
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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Sentiment Analysis of Emotional Intensity as a Continuous Driver of Engagement and Algorithmic Visibility Zia Ul Rehman Zafar; Dedi Gunawan; Muhammad Saif
Nusantara Journal of Artificial Intelligence and Information Systems Vol. 2 No. 1 (2026): June
Publisher : Faculty of Engineering and Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47776/nuai.v2i1.2006

Abstract

This study investigates how emotional intensity, rather than sentiment direction, shapes engagement and algorithmic visibility in digital political discourse. Using sentiment analysis, a dataset of about 15,000 posts from Twitter (X) and YouTube was collected over a 30-day period and scored with a hybrid TextBlob, VADER, and BERT pipeline. Emotional strength (the absolute sentiment value) correlated moderately with engagement (r = 0.58, p < 0.05), whereas the directional sentiment score did not (r ≈ 0.05). Emotionally intense posts attracted about 2.4 times more engagement than neutral posts, and positive posts were the most frequent (41%) while neutral posts drew the lowest mean engagement. These results indicate that engagement-based ranking amplifies emotional magnitude over neutral or analytical content, which can narrow the diversity of visible expression. The findings give platform designers and policymakers a reproducible basis for assessing how affective dynamics shape visibility in algorithmically mediated public discourse.
Cognitive Roots of Customer Engagement: Sentiment-Driven Decision Dynamics in Social Media Marketing of Indonesian SMEs Zia Ul Rehman Zafar; Ahmad Hassan; Ahad Sultan; Khasif Ali Abdul Wahid Alias
Journal of Business Management and Islamic Banking Vol.5 No.1 (2026)
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jbmib.2026.0501-04

Abstract

Purpose—This study addresses the growing disconnect between high social media engagement and actionable decision-making outcomes among Indonesian SMEs. While firms generate large volumes of interaction data, they often lack the analytical frameworks to interpret customer behavior.  Design/methodology/approach—To address this issue, the study proposes a cognitive-behavioral model integrating natural language processing (NLP)-based sentiment analysis with engagement metrics.  Findings—Using a dataset of 195,513 social media interactions, regression results indicate that sentiment significantly influences engagement (β = 0.43, p < 0.01), while engagement strongly predicts decision outcomes (β = 0.51, p < 0.01). Mediation analysis confirms that engagement partially transmits the effect of sentiment (indirect effect = 0.22).  Research implication/limitation—The findings demonstrate that sentiment-driven engagement serves as a critical mechanism linking digital interactions to behavioral outcomes, offering a data-driven foundation for adaptive marketing strategies in SMEs. Originality/value—The novelty lies in conceptualizing engagement as an observable decision-making process rooted in cognitive sentiment dynamics.
Co-Authors Ahad Sultan Ahmad Hassan Dedi Gunawan Khasif Ali Abdul Wahid Alias Muhammad Saif