With the rapid growth of unstructured data from social media, reviews, and forums, text mining has become essential in Information Systems (IS) for extracting actionable insights. Summarization can condense fragmented, emotion-rich posts, but existing methods-optimized for structured news-struggle with noisy, informal content. Emotional cues are critical for IS tasks such as brand monitoring and market analysis, yet few studies integrate sentiment modeling into summarization of short user-generated texts. We propose a sentiment-aware framework extending extractive (TextRank) and abstractive (UniLM) approaches by embedding sentiment signals into ranking and generation processes. This dual design improves the capture of emotional nuances and thematic relevance, producing concise, sentiment-enriched summaries that enhance timely interventions and strategic decision-making in dynamic online environments.

翻译：随着社交媒体、评论和论坛中非结构化数据的快速增长，文本挖掘已成为信息系统（IS）中提取可操作见解的关键技术。摘要技术能够浓缩零散且富含情感的帖子，但现有方法——主要针对结构化新闻优化——在处理嘈杂、非正式内容时面临挑战。情感线索对于品牌监控和市场分析等IS任务至关重要，然而将情感建模整合到短文本用户生成内容的摘要中的研究却很少。我们提出了一种情感感知框架，通过将情感信号嵌入排序和生成过程，扩展了抽取式（TextRank）和生成式（UniLM）方法。这种双重设计提升了对情感细微差别和主题相关性的捕捉能力，从而生成简洁且富含情感的摘要，有助于在动态在线环境中增强及时干预和战略决策。