A model for the Twitter sentiment curve

TL;DR

This paper introduces a sentiment dynamics model based on a local reinforcement Pólya urn to predict and analyze Twitter sentiment trends related to political topics, capturing fluctuations and emotional sensitivities.

Contribution

The paper presents a novel Pólya urn variant with local reinforcement and fluctuation modeling, specifically tailored for Twitter sentiment analysis and prediction.

Findings

The model accurately reproduces sentiment dynamics on Twitter data.

It outperforms standard Pólya urn models in predictive tasks.

Different datasets reveal varying emotional sensitivities to public events.

Abstract

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 P\'olya 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 P\'olya urn model. Moreover, the different performances on different data sets highlight different emotional sensitivities respect to a public event.