追踪聊天对话中的情绪动态：一种结合DistilBERT与表情符号情感分析的混合方法
Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis
AI总结：
本文提出结合DistilBERT文本情绪检测与表情符号情感分析的混合方法，用于实时追踪聊天对话中的情绪动态，并在Twitter数据集上验证其优于SVM、Random Forest和AdaBoost的性能。
摘要：
在许多情境下，以计算机为媒介的沟通已变得比面对面沟通更为重要。追踪聊天对话中的情绪动态可以增强沟通、改善服务并在多种情境中支持心理健康。本文探索了一种混合方法，通过结合基于DistilBERT的文本情绪检测与表情符号情感分析来追踪聊天对话中的情绪动态。使用多种机器学习算法（包括SVM、Random Forest和AdaBoost）对一个Twitter数据集进行了分析。我们将它们的性能与DistilBERT进行了对比。结果表明，DistilBERT在情绪识别方面具有更优越的性能。我们的方法考虑了通过表情符号传达的情感表达，以更好地理解参与者在聊天过程中的情绪。我们展示了该方法如何能够有效捕捉和分析实时对话中的情绪变化。我们的研究结果表明，整合文本与表情符号分析是追踪聊天情绪的有效方式，在客户服务、工作聊天和社交媒体互动中具有潜在应用价值。
英文摘要：
Computer-mediated communication has become more important than face-to-face communication in many contexts. Tracking emotional dynamics in chat conversations can enhance communication, improve services, and support well-being in various contexts. This paper explores a hybrid approach to tracking emotional dynamics in chat conversations by combining DistilBERT-based text emotion detection and emoji sentiment analysis. A Twitter dataset was analyzed using various machine learning algorithms, including SVM, Random Forest, and AdaBoost. We contrasted their performance with DistilBERT. Results reveal DistilBERT's superior performance in emotion recognition. Our approach accounts for emotive expressions conveyed through emojis to better understand participants' emotions during chats. We demonstrate how this approach can effectively capture and analyze emotional shifts in real-time conversations. Our findings show that integrating text and emoji analysis is an effective way of tracking chat emotion, with possible applications in customer service, work chats, and social media interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
