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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.18383">arXiv:2411.18383</a> <span> [<a href="https://arxiv.org/pdf/2411.18383">pdf</a>, <a href="https://arxiv.org/format/2411.18383">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Topic Modeling and Sentiment Analysis on Japanese Online Media's Coverage of Nuclear Energy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sun%2C+Y">Yifan Sun</a>, <a href="/search/cs?searchtype=author&query=Tsuruta%2C+H">Hirofumi Tsuruta</a>, <a href="/search/cs?searchtype=author&query=Kumagai%2C+M">Masaya Kumagai</a>, <a href="/search/cs?searchtype=author&query=Kurosaki%2C+K">Ken Kurosaki</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.18383v1-abstract-short" style="display: inline;"> Thirteen years after the Fukushima Daiichi nuclear power plant accident, Japan's nuclear energy accounts for only approximately 6% of electricity production, as most nuclear plants remain shut down. To revitalize the nuclear industry and achieve sustainable development goals, effective communication with Japanese citizens, grounded in an accurate understanding of public sentiment, is of paramount… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.18383v1-abstract-full').style.display = 'inline'; document.getElementById('2411.18383v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.18383v1-abstract-full" style="display: none;"> Thirteen years after the Fukushima Daiichi nuclear power plant accident, Japan's nuclear energy accounts for only approximately 6% of electricity production, as most nuclear plants remain shut down. To revitalize the nuclear industry and achieve sustainable development goals, effective communication with Japanese citizens, grounded in an accurate understanding of public sentiment, is of paramount importance. While nationwide surveys have traditionally been used to gauge public views, the rise of social media in recent years has provided a promising new avenue for understanding public sentiment. To explore domestic sentiment on nuclear energy-related issues expressed online, we analyzed the content and comments of over 3,000 YouTube videos covering topics related to nuclear energy. Topic modeling was used to extract the main topics from the videos, and sentiment analysis with large language models classified user sentiments towards each topic. Additionally, word co-occurrence network analysis was performed to examine the shift in online discussions during August and September 2023 regarding the release of treated water. Overall, our results provide valuable insights into the online discourse on nuclear energy and contribute to a more comprehensive understanding of public sentiment in Japan. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.18383v1-abstract-full').style.display = 'none'; document.getElementById('2411.18383v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 9 figures, 4 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.06852">arXiv:2310.06852</a> <span> [<a href="https://arxiv.org/pdf/2310.06852">pdf</a>, <a href="https://arxiv.org/format/2310.06852">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tsuruta%2C+H">Hirofumi Tsuruta</a>, <a href="/search/cs?searchtype=author&query=Katsura%2C+Y">Yukari Katsura</a>, <a href="/search/cs?searchtype=author&query=Kumagai%2C+M">Masaya Kumagai</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.06852v1-abstract-short" style="display: inline;"> Machine learning (ML) is becoming increasingly popular for predicting material properties to accelerate materials discovery. Because material properties are strongly affected by its crystal structure, a key issue is converting the crystal structure into the features for input to the ML model. Currently, the most common method is to convert the crystal structure into a graph and predicting its prop… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.06852v1-abstract-full').style.display = 'inline'; document.getElementById('2310.06852v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.06852v1-abstract-full" style="display: none;"> Machine learning (ML) is becoming increasingly popular for predicting material properties to accelerate materials discovery. Because material properties are strongly affected by its crystal structure, a key issue is converting the crystal structure into the features for input to the ML model. Currently, the most common method is to convert the crystal structure into a graph and predicting its properties using a graph neural network (GNN). Some GNN models, such as crystal graph convolutional neural network (CGCNN) and atomistic line graph neural network (ALIGNN), have achieved highly accurate predictions of material properties. Despite these successes, using a graph to represent a crystal structure has the notable limitation of losing the crystal structure's three-dimensional (3D) information. In this work, we propose DeepCrysTet, a novel deep learning approach for predicting material properties, which uses crystal structures represented as a 3D tetrahedral mesh generated by Delaunay tetrahedralization. DeepCrysTet provides a useful framework that includes a 3D mesh generation method, mesh-based feature design, and neural network design. The experimental results using the Materials Project dataset show that DeepCrysTet significantly outperforms existing GNN models in classifying crystal structures and achieves state-of-the-art performance in predicting elastic properties. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.06852v1-abstract-full').style.display = 'none'; document.getElementById('2310.06852v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" 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