基于熵增长的在线情感对话模型
Entropy-growth-based model of emotionally charged online dialogues
摘要：
我们从IRC（互联网中继聊天）和模型的参与者之间的对话，假设讨论的驱动力是情绪概率分布的熵增长的情感注释的海量数据进行分析。这个过程被认为与对话中观察到的讨论长度的幂律分布的出现有关。我们进行了数值模拟的基础上注意到的现象，获得了良好的协议与实际数据。最后，我们提出了一种依赖于情绪概率分布熵的方法来人为地延长讨论的持续时间。
英文摘要：
We analyze emotionally annotated massive data from IRC (Internet Relay Chat) and model the dialogues between its participants by assuming that the driving force for the discussion is the entropy growth of emotional probability distribution. This process is claimed to be correlated to the emergence of the power-law distribution of the discussion lengths observed in the dialogues. We perform numerical simulations based on the noticed phenomenon obtaining a good agreement with the real data. Finally, we propose a method to artificially prolong the duration of the discussion that relies on the entropy of emotional probability distribution.
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。
