Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
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This Author published in this journals
All Journal Apollo: Journal of Tourism and Business
Vanessa Gaffar
Faculty of Economics and Business Education, Universitas Pendidikan Indonesia, Bandung, Indonesia
Economics, Econometrics & Finance Social Sciences

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Analyzing Consumer Purchase Decisions Toward Eyeshadow Products Through Sentiment Analysis and Social Network Analysis on X (Twitter) Auliya Nur Afifah; Vanessa Gaffar; Asep Miftahuddin
Apollo: Journal of Tourism and Business Vol. 4 No. 3 (2026): September 2026
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/apollo.v4i3.690

Abstract

This study examined the consumers' decisions on how to purchase eyeshadow products from Twitter/X conversations using Sentiment Analysis and Social Network Analysis (SNA). The research aimed to investigate the sentiment of consumers, interaction, influence of influential people, and communication structures of eyeshadow products in the social media marketing and the social media communication. We employed data collected from Twitter/X through SocialX as a quantitative computational social science approach to the research. The dataset comprised 218 records between January 1, 2026, and April 30, 2026, with eyeshadow, e.g., eye-shadow, eye-shadow review, eye-shadow recommendation, and eye-shadow discussion. We combined sentiment analysis, emotion analysis, text network analysis, trend analysis, and social network analysis to investigate consumers’ interactions and information sharing. We find that Twitter/X discussions regarding eyeshadow products are dominated by suggestions, product reviews, and information sharing. As a result, the sentiment analysis showed that neutral and positive sentiment dominated the discussion and the emotional representation was the most dominant emotion. The Social Network Analysis identified several key actors who are in the network and were involved in connecting discussion clusters and communicating cosmetic information. The findings show that the social media interactions were a major factor in the understanding of consumers’ decision to purchase eyeshadow products. The recommendations, positive perception of the product, and the well-connected digital communication structures of Twitter/X have shown that Twitter/X is a very effective source to exchange product information and to shape cosmetic purchasing in beauty online communities.
Mapping Consumer Sentiment and Network Dynamics of Perfume Co-Branding Discourse on X/Twitter: A Computational Social Network Analysis Approach Fida Adzkiyatunnida; Vanessa Gaffar; Asep Miftahuddin
Apollo: Journal of Tourism and Business Vol. 4 No. 3 (2026): September 2026
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/apollo.v4i3.691

Abstract

The rapid growth of the global fragrance industry has driven brands to adopt co-branding strategies to strengthen brand equity and expand market reach, yet consumer responses to these collaborations in digital spaces remain fragmented and difficult to predict. This study analyzes the distribution of consumer sentiment, identifies key actors in interaction networks, and explores the extent to which Twitter-based data can complement the evaluation of fragrance co-branding strategy. Using an exploratory social media analytics approach grounded in Digital Public Sphere theory and Social Network Theory, this study integrates Social Network Analysis with six computational modules, wordcloud analysis, sentiment analysis, text network analysis, emotion analysis, trend analysis, and zero-shot classification, applied to 300 public tweets collected via the SocialX platform during 1–12 January 2026 using fragrance and brand collaboration keywords. Results show that public discourse was lexically dominated by neutral sentiment (88%), while zero-shot classification of the same corpus yielded a positive-leaning distribution (82.33%); these are interpreted as two distinct constructs, evaluative polarity versus semantic stance, alongside a Sentiment Index of +0.28 and a dominant happy-emotion classification (92%). Network analysis identified a small number of actors occupying central or bridging positions in information dissemination, and trend analysis detected two major activity peaks on January 1–2, 2026, coinciding with the New Year transition. These findings offer theoretical implications for applying Digital Public Sphere and Social Network Theory jointly to fragrance co-branding discourse, and practical implications for brand managers seeking to monitor how collaboration discourse is expressed and circulates online.
Mapping Market Risk Narratives of Artificial Intelligence: A Social Network Analysis of Public Discourse Kirana Labbaika; Vanessa Gaffar; Asep Miftahuddin
Apollo: Journal of Tourism and Business Vol. 4 No. 3 (2026): September 2026
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/apollo.v4i3.717

Abstract

As AI technologies rapidly progress, privacy and surveillance, cybersecurity, and governance have become increasingly pressing concerns that raise questions about how the narratives of risk and AI influence the market's perception of new technologies. As a component of the public discourse, it is becoming more and more a sign of trust or distrust in technology and a source of legitimation in the digital world. This study utilized a Social Network Analysis (SNA) and Text based analytical method to analyze the perception of the market on AI by analyzing the AI risk narratives in the online public discourse. Data were gathered from X (formerly Twitter) using the SocialX platform over the first quarter of 2026 (1st January-31st March 2026) and found to be 2,883 public posts. The analysis combined trend analysis, sentiment analysis, temporal word analysis, text network analysis, BERTopic topic modeling, zero-shot classification, and SNA. The results showed an attitude of caution, with neutral (57.86%) and negative sentiment (35.93%) dominating the AI risk discourse, suggesting that the public isn't entirely positive about AI. Most prevalent data privacy issues were Data Privacy Risk (43.81%), Surveillance and Control Risk (21.19%) and Cybersecurity Risk (11.24%). Text network analysis further identified data, training, and surveillance as central themes within the discourse. This study contributes theoretically by integrating Framing Theory, Perceived Risk Theory, and market legitimacy perspectives to explain AI risk narratives as indicators of market perception. In practical terms, the results offer insights for trust-building in AI, responsible communication, and technology branding strategies.
Co-Authors Asep Miftahuddin Auliya Nur Afifah Fida Adzkiyatunnida Kirana Labbaika