基于Twitter数据的表情符号-情感排序系统
Emoji-Emotion Ranking System Using Twitter Data
- KBTU
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究基于10万条Twitter回复数据，提出表情符号情感分析框架，通过文本情感分类和情感聚合，构建表情符号-情感排序系统，并投影到Russell空间，证明表情符号具有概率性和上下文敏感的情感特征。
AI中文摘要：
如今，表情符号常常取代文字。然而，计算系统仍将其过度简化。大多数现有方法将表情符号视为静态情感指示器，忽视了其情感分布。在本研究中，我们提出了一种基于表情符号的情感分析框架，使用2020年至2025年间收集的包含100,000条表情符号回复的Twitter（X）数据集。经过预处理和文本清洗后，我们应用文本到情感分类来检测每条消息的五种基本情感（快乐、愤怒、悲伤、恐惧和惊讶）。通过聚合每个表情符号出现上下文中的情感分数，我们估计表情符号-情感关联分布，并构建一个反映相对情感优势的表情符号-情感排序系统。此外，我们将表情符号投影到Russell效价-唤醒空间，以实现连续的情感解释。我们的结果表明，表情符号表现出概率性、上下文敏感的情感特征，而非固定的情感极性。
英文摘要：
Nowadays, emojis are often replacing words. Yet computational systems still oversimplify them. Most existing approaches treat emojis as static sentiment indicators and overlook their emotional distributions. In this study, we propose an emoji-aware emotion analysis framework based on a Twitter (X) dataset of 100,000 emoji-containing replies collected between 2020-2025. After preprocessing and text cleaning, we applied text-to-emotion classification to detect five primary emotions (Happy, Angry, Sad, Fear, and Surprise) for each message. By aggregating emotion scores across contexts in which each emoji appears, we estimate emoji-emotion association distributions and construct an emoji-emotion ranking system reflecting relative emotional dominance. Furthermore, we project emojis into the Russell valence-arousal space to enable continuous affective interpretation. Our results demonstrate that emojis exhibit probabilistic, context-sensitive emotional profiles rather than fixed sentiment polarities.
