Network analysis is increasingly important across various fields, including the fragrance industry, where perfumes are represented as nodes and shared user preferences as edges in perfume networks. Community detection can uncover clusters of similar perfumes, providing insights into consumer preferences, enhancing recommendation systems, and informing targeted marketing strategies. This study aims to apply community detection techniques to group perfumes favored by users into relevant clusters for better recommendations. We constructed a bipartite network from user reviews on the Persian retail platform "Atrafshan," with nodes representing users and perfumes, and edges formed by positive comments. This network was transformed into a Perfume Co-Preference Network, connecting perfumes liked by the same users. By applying community detection algorithms, we identified clusters based on shared preferences, enhancing our understanding of user sentiment in the fragrance market. To improve sentiment analysis, we integrated emojis and a user voting system for greater accuracy. Emojis, aligned with their Persian counterparts, captured the emotional tone of reviews, while user ratings for scent, longevity, and sillage refined sentiment classification. Edge weights were adjusted by combining adjacency values with user ratings in a 60:40 ratio, reflecting both connection strength and user preferences. These enhancements led to improved modularity of detected communities, resulting in more accurate perfume groupings. This research pioneers the use of community detection in perfume networks, offering new insights into consumer preferences. Our advancements in sentiment analysis and edge weight refinement provide actionable insights for optimizing product recommendations and marketing strategies in the fragrance industry.

翻译：网络分析在包括香水行业在内的多个领域中日益重要，其中香水被表示为香水网络中的节点，而共享的用户偏好则构成边。社区发现能够揭示相似香水的聚类，从而为理解消费者偏好、改进推荐系统以及制定精准营销策略提供洞见。本研究旨在应用社区检测技术，将用户喜爱的香水分组为相关聚类，以实现更优的推荐。我们基于波斯零售平台“Atrafshan”的用户评论构建了一个二分网络，其中节点代表用户和香水，边则由积极评论构成。该网络被转化为一个香水共偏好网络，连接了同一用户喜爱的香水。通过应用社区检测算法，我们基于共享偏好识别出聚类，从而加深了对香水市场中用户情感的理解。为改进情感分析，我们整合了表情符号和用户投票系统以提高准确性。表情符号与其波斯语对应情感保持一致，捕捉了评论的情感基调，而用户对香气、持久度和扩散度的评分则细化了情感分类。通过将邻接值与用户评分按60:40的比例结合来调整边权重，以同时反映连接强度和用户偏好。这些改进提升了检测社区的模块度，从而实现了更精确的香水分组。本研究开创了社区检测在香水网络中的应用，为消费者偏好提供了新的见解。我们在情感分析和边权重优化方面的进展，为香水行业优化产品推荐和营销策略提供了可操作的洞见。