Sentiment Analysis on Social Media Review Texts from the Perspective of Appraisal Theory: A Case Study of Tao Tao Ju Brand on Xiaohongshu

Mapping Consumer Sentiment and Network Dynamics of Perfume Co-Branding Discourse on X/Twitter: A Computational Social Network Analysis Approach

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.

Social Media Sentiment Analysis of Intangible Assets in SME Success: The Role of Connection, Ethical Principles, and Partnerships

Small and Medium Enterprises (SMEs) have been the subject of various practitioner and academic research to better understand the underlying reasons for success and failure. In the Malaysian context, focusing on non-financial factors, three key themes emerge that are associated with SME success: connections, ethical principles, and partnerships. This study explores their individual and collective roles in SME success by capturing real-time perceptions from identified stakeholders through sentiment analysis of social media comments. Based on a curated sample of 58 comments from the Entrepreneurship and Startups in Malaysia Facebook group, the research integrated Natural Language Processing (NLP) models to detect expressions such as sarcasm and irony, along with manual thematic validation. This study revealed distinctive sentiment patterns among SMEs, with positive sentiment concentrated in themes related to employee reliability and intrinsic motivation, the integration of ethics into everyday operations and systems, and collaboration with complementary partnerships. This study demonstrated the strength of sentiment analysis of organic digital communications as a means of scaling, unobtrusively, and continuously diagnosing the health of an SME’s vital intangible ecosystem. The resulting framework proactively monitors and fosters relational pillars, enabling a shift from intuitive management to evidence-based interventions.

Sentiment Analysis of Selected Comments on #SaveGaza on Tiktok Posts

This study examines the sentiment expressed in TikTok comments under the hashtag #SaveGaza to explore the emotional dynamics and key themes within this digital public. TikTok’s unique short-video format and predominantly young user base provide a distinctive environment for political and humanitarian discourse. Sentiment analysis, which classifies text into positive, negative, or neutral categories, was applied to a 100 sampled dataset of highly engaged comments on #SaveGaza posts. The analysis revealed that negative sentiment dominated, reflecting widespread anger, grief, and frustration related to the Gaza crisis. Positive sentiment was also significant, expressing solidarity, hope, and calls for peace, while neutral comments provided factual context and historical information. Key themes identified include activism, media criticism, personal storytelling, and human rights advocacy. These findings align with the concept of affective publics, where shared emotional expression on digital platforms fosters politically engaged communities. The study highlights the challenges of sentiment analysis on TikTok due to informal language, slang, emojis, and evolving online vernacular, suggesting the need for hybrid approaches combining automated tools with manual interpretation. Overall, this research contributes to understanding how sentiment analysis can be adapted to TikTok’s environment and underscores the platform’s role as a space for effective political engagement. The insights have practical implications for activists, policymakers, and scholars interested in digital public discourse on emerging social media platforms.

The Chinese translation and reception of The Alchemist : A python-based sentiment analysis and topic modeling of Douban reader reviews

Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles

While sentiment analysis has matured from an experimental technique to a core methodology in communication, it risks methodological stagnation due to data source limitations, and future research is suggested to focus on multimodal analysis and diverse digital platforms to overcome these constraints.

Positive Sentiment Dominance and Audience Polarization: An Analysis of Prabowo–Gibran’s Political Content on Fero Walandouw’s Instagram

This study examines public sentiment toward the Prabowo–Gibran presidential pair in influencer-based political content on Instagram during the 2024 Indonesian Presidential Election. Existing studies on digital political communication in Indonesia generally focus on official campaign accounts or political branding, leaving individual influencers and audience responses relatively underexplored. Grounded in the concepts of political personalization and influencer-based political communication, this research aims to identify public sentiment patterns in content featuring influencer Fero Walandouw and to compare sentiment between collaborative and independent posts. Using a quantitative computational social science approach, the study applies sentiment analysis to Instagram comments collected through systematic scraping during the campaign period. Public comments were classified into positive, negative, and neutral categories using natural language processing techniques tailored for Indonesian social media data. The findings reveal that positive sentiment dominates audience responses to Prabowo–Gibran. However, collaborative posts generated higher levels of negative sentiment and audience polarization compared to independent content, which yielded more positive responses. This research contributes to digital political communication literature by demonstrating that different forms of influencer-based content produce distinct audience evaluation patterns and political sentiment on social media.Penelitian ini mengkaji sentimen publik terhadap pasangan Prabowo–Gibran dalam konten politik berbasis influencer di Instagram selama Pilpres Indonesia 2024. Studi komunikasi politik digital di Indonesia umumnya berfokus pada akun resmi atau branding politik, sehingga analisis terhadap influencer individual dan respons audiens masih terbatas. Menggunakan konsep personalisasi dan komunikasi politik berbasis influencer, penelitian ini bertujuan mengidentifikasi pola sentimen publik dalam konten influencer Fero Walandouw, serta membandingkan pola sentimen antara unggahan kolaboratif dan independen. Penelitian kuantitatif berbasis computational social science ini menerapkan analisis sentimen terhadap komentar Instagram yang dikumpulkan melalui scraping sistematis selama periode kampanye. Komentar diklasifikasikan ke dalam kategori positif, negatif, dan netral menggunakan teknik natural language processing berbahasa Indonesia. Hasil penelitian menunjukkan bahwa sentimen positif mendominasi respons audiens terhadap Prabowo–Gibran. Namun, unggahan kolaboratif memicu tingkat sentimen negatif dan polarisasi audiens yang lebih tinggi dibandingkan konten independen, yang cenderung direspon lebih positif. Penelitian ini berkontribusi pada literatur komunikasi politik digital dengan membuktikan bahwa berbagai bentuk konten berbasis influencer menghasilkan pola evaluasi audiens dan sentimen politik yang berbeda di media sosial.