论文标题
Twitter情感曲线的模型
A model for the Twitter sentiment curve
论文作者
论文摘要
由于其信息（特别适合政治口号）和信息的快速传播，Twitter是政治沟通最常用的在线平台之一。尤其是当该论点刺激用户的情绪时，Twitter上的内容以极高的速度共享，从而研究推文情感，如果最重要的是预测讨论的演变和相对叙述的登记册。在本文中，我们提出了一个模型，能够重现与特定主题和时期有关的推文情感的动态，并根据观察到的过去对未来帖子的情感进行预测。该模型是pólyaurn的最新变体，在Arxiv中引入和研究了：1906.10951和Arxiv：2010.06373，其特征是“局部”强化，即强化机制，主要是基于最新观察结果，以及经常的持续观察结果，并具有预测性的均值波动。特别是，后一个功能能够捕获情感曲线中的趋势波动。虽然提出的模型非常笼统，并且也可以在其他情况下使用，但已在几个Twitter数据集上进行了测试，并且与标准Pólyaurn模型相比，其性能更高。此外，不同数据集的不同性能突出了公共事件的不同情感敏感性。
Twitter is among the most used online platforms for the political communications, due to the concision of its messages (which is particularly suitable for political slogans) and the quick diffusion of messages. Especially when the argument stimulate the emotionality of users, the content on Twitter is shared with extreme speed and thus studying the tweet sentiment if of utmost importance to predict the evolution of the discussions and the register of the relative narratives. In this article, we present a model able to reproduce the dynamics of the sentiments of tweets related to specific topics and periods and to provide a prediction of the sentiment of the future posts based on the observed past. The model is a recent variant of the Pólya urn, introduced and studied in arXiv:1906.10951 and arXiv:2010.06373, which is characterized by a "local" reinforcement, i.e. a reinforcement mechanism mainly based on the most recent observations, and by a random persistent fluctuation of the predictive mean. In particular, this latter feature is capable of capturing the trend fluctuations in the sentiment curve. While the proposed model is extremely general and may be also employed in other contexts, it has been tested on several Twitter data sets and demonstrated greater performances compared to the standard Pólya urn model. Moreover, the different performances on different data sets highlight different emotional sensitivities respect to a public event.
