Search

This study explores how different weather conditions influence public sentiment on social media, focusing on Twitter data from the UK. By considering climate and linguistic baselines, we improve the accuracy of weather-related sentiment analysis. Our findings show that emotional responses to weather are complex, influenced by combinations of weather variables and regional language differences. The results highlight the importance of context-sensitive methods for better understanding public mood in response to weather, which can enhance impact-based forecasting and risk communication in the context of climate change., Comment: 12 pages, 5 figures

The well-known ponderomotive force, arising from the interaction of matter and light, has critical implications across a broad range of fields from laser fusion and astrophysics to laser diagnostics and even pulsed-power experiments. This pseudo-potential pushes electrons, which through coulomb forces causes ion density modulations that can steepen with profound implications. When used intentionally, density modulations can be used for plasma gratings, which are essential for optical components operating in extreme conditions for next generation lasers. They can also be important for plasma confinement and particle trapping, which can even impact magnetic confinement in fusion devices. The ponderomotive potential also leads to laser self-focusing, complicating laser diagnostics. In laser fusion, the force exacerbates challenges posed by stimulated Brillouin scattering (SBS) and crossed beam energy transfer (CBET), both of which destabilize the fusion process. It even plays an astrophysical role in the filamentation of fast radio bursts in the relativistic winds of magnetars. Since the ponderomotive force primarily effects electron dynamics, multi-fluid/particle codes or additional ansatz are required to include its effects. This paper demonstrates that by including electron effects on an ion timescale with a 1-fluid, 2-energy extended magnetohydrodynamics (XMHD) model, ponderomotive effects are also naturally present. We introduce the theory for these dynamics and demonstrate their presence with 1-D pencil-like simulations.

High-power lasers are at the forefront of science in many domains. While their fields are still far from reaching the Schwinger limit, they have been used in extreme regimes, to successfully accelerate particles at high energies, or to reproduce phenomena observed in astrophysical settings. However, our understanding of laser plasma interactions is limited by numerical simulations, which are very expensive to run as short temporal and spatial scales need to be resolved explicitly. Under such circumstances, a non-collisional approach to model laser-plasma interactions becomes numerically expensive. Even a collisional approach, modeling the electrons and ions as independent fluids, is slow in practice. In both cases, the limitation comes from a direct computation of electron motion. In this work, we show how the generalized Ohm's law captures collisional absorption phenomena through the macroscopic interactions of laser fields, electron flows, and ion dynamics. This approach replicates several features usually associated with explicit electron motion, such as cut-off density, reflection and absorption. As the electron dynamics is now solved implicitly, the spatial and temporal scales of this model fit well between multi-fluid and standard magnetohydrodynamics scales, allowing to study a new class of problems that would be too expensive to solve numerically with other methods.

Researchers commonly perform sentiment analysis on large collections of short texts like tweets, Reddit posts or newspaper headlines that are all focused on a specific topic, theme or event. Usually, general-purpose sentiment analysis methods are used. These perform well on average but miss the variation in meaning that happens across different contexts, for example, the word "active" has a very different intention and valence in the phrase "active lifestyle" versus "active volcano". This work presents a new approach, CIDER (Context Informed Dictionary and sEmantic Reasoner), which performs context-sensitive linguistic analysis, where the valence of sentiment-laden terms is inferred from the whole corpus before being used to score the individual texts. In this paper, we detail the CIDER algorithm and demonstrate that it outperforms state-of-the-art generalist unsupervised sentiment analysis techniques on a large collection of tweets about the weather. CIDER is also applicable to alternative (non-sentiment) linguistic scales. A case study on gender in the UK is presented, with the identification of highly gendered and sentiment-laden days. We have made our implementation of CIDER available as a Python package: https://pypi.org/project/ciderpolarity/., Comment: 20 pages, 6 figures, 3 tables