Low-resource African languages remain underrepresented in sentiment analysis, limiting both lexical coverage and the performance of multilingual Natural Language Processing (NLP) systems. This study proposes TriLex, a three-stage retrieval augmented framework that unifies corpus-based extraction, cross lingual mapping, and retrieval augmented generation (RAG) driven lexical refinement to systematically expand sentiment lexicons for low-resource languages. Using the enriched lexicon, the performance of two prominent African pretrained language models (AfroXLMR and AfriBERTa) is evaluated across multiple case studies. Results demonstrate that AfroXLMR delivers superior performance, achieving F1-scores above 80% for isiXhosa and isiZulu and exhibiting strong cross-lingual stability. Although AfriBERTa lacks pre-training on these target languages, it still achieves reliable F1-scores around 64%, validating its utility in computationally constrained settings. Both models outperform traditional machine learning baselines, and ensemble analyses further enhance precision and robustness. The findings establish TriLex as a scalable and effective framework for multilingual sentiment lexicon expansion and sentiment modeling in low-resource South African languages.
非洲低资源语言在情感分析中仍处于代表性不足的状态，这限制了词典的词汇覆盖范围以及多语种自然语言处理（NLP）系统的性能。本研究提出了TriLex，这是一种三阶段检索增强框架，它将基于语料库的提取、跨语言映射和检索增强生成（RAG）驱动的词汇精炼统一起来，系统地扩展了低资源语言的情感词典。使用丰富后的词典，评估了两个在非洲流行的预训练语言模型（AfroXLMR 和 AfriBERTa）在多个案例研究中的表现。结果显示，AfroXLMR 表现更优，在 isiXhosa 和 isiZulu 语种上实现了超过80%的 F1 分数，并且展示了强大的跨语言稳定性。尽管 AfriBERTa 没有对这些目标语言进行预训练，它仍然取得了大约64%的可靠F1分数，这证明了其在计算资源受限环境中的实用性。这两种模型都优于传统的机器学习基线，并且集合分析进一步提升了精确度和鲁棒性。研究结果确立了 TriLex 作为一种可扩展且有效的多语言情感词典扩展框架以及低资源南非语言的情感建模方法的地位。
https://arxiv.org/abs/2512.02799
This study presents a comprehensive analysis of public sentiment toward traffic management policies in Knoxville, Tennessee, utilizing social media data from Twitter and Reddit platforms. We collected and analyzed 7906 posts spanning January 2022 to December 2023, employing Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modeling. Our findings reveal predominantly negative sentiment, with significant variations across platforms and topics. Twitter exhibited more negative sentiment compared to Reddit. Topic modeling identified six distinct themes, with construction-related topics showing the most negative sentiment while general traffic discussions were more positive. Spatiotemporal analysis revealed geographic and temporal patterns in sentiment expression. The research demonstrates social media's potential as a real-time public sentiment monitoring tool for transportation planning and policy evaluation.
这项研究对田纳西州诺克斯维尔市交通管理政策的公众情绪进行了全面分析，利用了来自Twitter和Reddit平台上的社交媒体数据。我们收集并分析了从2022年1月至2023年12月期间发布的7906条帖子，并使用带有情感意识词典与情感推理器（VADER）进行情感分析，并采用潜在狄利克雷分配（LDA）模型进行主题建模。研究结果表明，公众情绪总体上以负面为主，但不同平台和话题之间存在显著差异。Twitter平台上表达的负面情绪比Reddit更多。主题建模识别出六个不同的主题类别，其中与建设相关的话题表现出最强烈的负面情绪，而关于一般交通问题的讨论则较为积极。时空分析揭示了情感表达在地理和时间上的模式变化。这项研究展示了社交媒体作为实时监测公共交通规划及政策评估中公众情绪工具的巨大潜力。
https://arxiv.org/abs/2512.03103
In this paper, we introduce Story2MIDI, a sequence-to-sequence Transformer-based model for generating emotion-aligned music from a given piece of text. To develop this model, we construct the Story2MIDI dataset by merging existing datasets for sentiment analysis from text and emotion classification in music. The resulting dataset contains pairs of text blurbs and music pieces that evoke the same emotions in the reader or listener. Despite the small scale of our dataset and limited computational resources, our results indicate that our model effectively learns emotion-relevant features in music and incorporates them into its generation process, producing samples with diverse emotional responses. We evaluate the generated outputs using objective musical metrics and a human listening study, confirming the model's ability to capture intended emotional cues.
在这篇论文中，我们介绍了Story2MIDI，这是一个基于序列到序列Transformer的模型，用于从给定文本生成与情感相匹配的音乐。为了开发这一模型，我们构建了Story2MIDI数据集，通过合并现有的文本情感分析和音乐情绪分类的数据集来创建。该数据集中包含了能够引起读者或听众相同情感反应的文字片段和音乐作品对。尽管我们的数据规模较小且计算资源有限，但结果表明，我们的模型有效地学习到了与情感相关的音乐特征，并将其纳入生成过程中，从而产生了具有多样情感回应的样本。我们使用客观音乐指标和人类听觉研究来评估生成的输出，确认了该模型捕捉预期情绪线索的能力。
https://arxiv.org/abs/2512.02192
Limited data for low-resource languages typically yield weaker language models (LMs). Since pre-training is compute-intensive, it is more pragmatic to target improvements during fine-tuning. In this work, we examine the use of Active Learning (AL) methods augmented by structured data selection strategies which we term 'Active Learning schedulers', to boost the fine-tuning process with a limited amount of training data. We connect the AL to data clustering and propose an integrated fine-tuning pipeline that systematically combines AL, clustering, and dynamic data selection schedulers to enhance model's performance. Experiments in the Slovak, Maltese, Icelandic and Turkish languages show that the use of clustering during the fine-tuning phase together with AL scheduling can simultaneously produce annotation savings up to 30% and performance improvements up to four F1 score points, while also providing better fine-tuning stability.
对于资源匮乏的语言，有限的数据通常会导致较弱的语言模型（LM）。由于预训练需要大量的计算资源，因此在微调阶段进行改进更为实际。在这项工作中，我们研究了通过结构化数据选择策略增强的主动学习（AL）方法的应用，我们将这些策略称为“主动学习调度器”，以利用有限的训练数据提升微调过程的效果。我们连接了主动学习与数据聚类，并提出了一种集成的微调管道，该管道系统地结合了主动学习、聚类和动态数据选择调度器来增强模型的表现。实验表明，在斯洛伐克语、马耳他语、冰岛语和土耳其语中的研究表明，在微调阶段使用聚类与AL调度相结合可以同时节省高达30%的标注成本，并提高多达四点F1评分，同时还能提供更好的微调稳定性。
https://arxiv.org/abs/2512.01460
Multimodal sentiment analysis (MSA) is a research field that recognizes human sentiments by combining textual, visual, and audio modalities. The main challenge lies in integrating sentiment-related information from different modalities, which typically arises during the unimodal feature extraction phase and the multimodal feature fusion phase. Existing methods extract only shallow information from unimodal features during the extraction phase, neglecting sentimental differences across different personalities. During the fusion phase, they directly merge the feature information from each modality without considering differences at the feature level. This ultimately affects the model's recognition performance. To address this problem, we propose a personality-sentiment aligned multi-level fusion framework. We introduce personality traits during the feature extraction phase and propose a novel personality-sentiment alignment method to obtain personalized sentiment embeddings from the textual modality for the first time. In the fusion phase, we introduce a novel multi-level fusion method. This method gradually integrates sentimental information from textual, visual, and audio modalities through multimodal pre-fusion and a multi-level enhanced fusion strategy. Our method has been evaluated through multiple experiments on two commonly used datasets, achieving state-of-the-art results.
多模态情感分析（MSA）是一个研究领域，它通过结合文本、视觉和音频等多种模式来识别人类的情感。该领域的主要挑战在于如何整合不同模式中的与情绪相关的信息，这一问题通常在单模式特征提取阶段和跨模式特征融合阶段出现。现有的方法在提取阶段仅从单模态特征中提取浅层信息，忽略了不同人格间情感的差异。而在融合阶段，则直接合并各模式的特征信息，没有考虑特征层面的区别。这最终影响了模型的情感识别性能。 为了解决这些问题，我们提出了一种性格-情绪对齐多级融合框架。在特征提取阶段引入人格特质，并提出了一个新颖的性格-情绪对齐方法，首次从文本模态中获得个性化的感情嵌入。在融合阶段，我们则提出了一种新的多级融合方法。通过跨模式预融合和多层次增强融合策略，该方法逐步将文本、视觉和音频模态中的情感信息整合起来。 我们的方法已经在两个常用数据集上进行了多种实验评估，并取得了最先进的结果。
https://arxiv.org/abs/2512.01442
Understanding sentiment in complex textual expressions remains a fundamental challenge in affective computing. To address this, we propose a Dynamic Fusion Learning Model (DyFuLM), a multimodal framework designed to capture both hierarchical semantic representations and fine-grained emotional nuances. DyFuLM introduces two key moodules: a Hierarchical Dynamic Fusion module that adaptively integrates multi-level features, and a Gated Feature Aggregation module that regulates cross-layer information ffow to achieve balanced representation learning. Comprehensive experiments on multi-task sentiment datasets demonstrate that DyFuLM achieves 82.64% coarse-grained and 68.48% fine-grained accuracy, yielding the lowest regression errors (MAE = 0.0674, MSE = 0.0082) and the highest R^2 coefficient of determination (R^2= 0.6903). Furthermore, the ablation study validates the effectiveness of each module in DyFuLM. When all modules are removed, the accuracy drops by 0.91% for coarse-grained and 0.68% for fine-grained tasks. Keeping only the gated fusion module causes decreases of 0.75% and 0.55%, while removing the dynamic loss mechanism results in drops of 0.78% and 0.26% for coarse-grained and fine-grained sentiment classification, respectively. These results demonstrate that each module contributes significantly to feature interaction and task balance. Overall, the experimental findings further validate that DyFuLM enhances sentiment representation and overall performance through effective hierarchical feature fusion.
理解复杂文本表达中的情感仍然是情感计算中的一个基本挑战。为了解决这一问题，我们提出了一种动态融合学习模型（DyFuLM），这是一种多模态框架，旨在捕捉层次化的语义表示和细微的情感差异。DyFuLM引入了两个关键模块：一种是层级动态融合模块，它能够自适应地整合多层次特征；另一种是有门控特性聚合的模块，该模块调节跨层信息流以实现平衡的表示学习。 在多任务情感数据集上的全面实验表明，DyFuLM实现了82.64%的粗粒度准确率和68.48%的细粒度准确率，并且获得了最低的回归误差（MAE = 0.0674，MSE = 0.0082）以及最高的R^2决定系数（R^2= 0.6903）。此外，消融实验验证了DyFuLM中每个模块的有效性。当移除所有模块时，粗粒度和细粒度任务的准确率分别下降了0.91% 和0.68%；仅保留门控融合模块导致准确率分别降低了0.75% 和0.55%，而去除动态损失机制则使粗粒度和细粒度情感分类的准确率分别下降了0.78% 和0.26%。这些结果表明，每个模块在特征交互和任务平衡中都发挥了重要作用。 总体而言，实验发现进一步验证了DyFuLM通过有效的层次化特征融合增强了情感表示和整体性能。
https://arxiv.org/abs/2512.01410
MARSAD is a multifunctional natural language processing (NLP) platform designed for real-time social media monitoring and analysis, with a particular focus on the Arabic-speaking world. It enables researchers and non-technical users alike to examine both live and archived social media content, producing detailed visualizations and reports across various dimensions, including sentiment analysis, emotion analysis, propaganda detection, fact-checking, and hate speech detection. The platform also provides secure data-scraping capabilities through API keys for accessing public social media data. MARSAD's backend architecture integrates flexible document storage with structured data management, ensuring efficient processing of large and multimodal datasets. Its user-friendly frontend supports seamless data upload and interaction.
MARSAD 是一个多用途的自然语言处理（NLP）平台，专为实时社交媒体监控和分析设计，尤其关注阿拉伯语世界。该平台使研究人员及非技术人员都能够分析实时和存档的社交媒体内容，并生成详细的可视化报告和跨多个维度的报告，包括情感分析、情绪分析、宣传检测、事实核查以及仇恨言论检测。此外，MARSAD 通过 API 密钥提供了安全的数据抓取功能，以便访问公开的社交媒体数据。 该平台的后端架构集成了灵活的文档存储与结构化数据管理，确保了大规模和多模态数据集的有效处理。其用户友好的前端支持无缝的数据上传和互动操作。
https://arxiv.org/abs/2512.01369
The Nagamese language, a.k.a Naga Pidgin, is an Assamese-lexified creole language developed primarily as a means of communication in trade between the people from Nagaland and people from Assam in the north-east India. Substantial amount of work in sentiment analysis has been done for resource-rich languages like English, Hindi, etc. However, no work has been done in Nagamese language. To the best of our knowledge, this is the first attempt on sentiment analysis and emotion classification for the Nagamese Language. The aim of this work is to detect sentiments in terms of polarity (positive, negative and neutral) and basic emotions contained in textual content of Nagamese language. We build sentiment polarity lexicon of 1,195 nagamese words and use these to build features along with additional features for supervised machine learning techniques using Na"ive Bayes and Support Vector Machines. Keywords: Nagamese, NLP, sentiment analysis, machine learning
纳加梅斯语，又称纳加皮钦语，是一种以阿萨姆语为基础的克里奥尔语，在印度东北部纳迦兰和阿萨姆邦的人们进行贸易交流时主要作为沟通手段而发展起来。对于英语、印地语等资源丰富的语言来说，情感分析的工作已经取得了大量进展。然而，关于纳加梅斯语的情感分析工作尚未有人开展。据我们所知，这项研究是首个针对纳加梅斯语进行情感分析和情绪分类的尝试。本研究旨在检测文本内容中表达出的积极、消极和中立的情绪极性以及基本情绪。我们构建了一个包含1,195个纳加梅斯词汇的情感极性词典，并利用这些词汇以及额外特征，通过朴素贝叶斯和支持向量机等监督机器学习技术进行特征建模。 关键词：纳加梅斯语、自然语言处理（NLP）、情感分析、机器学习
https://arxiv.org/abs/2512.01256
Brownfield engineering work involving legacy systems, incomplete documentation, and fragmented architectural knowledge poses unique challenges for the effective use of large language models (LLMs). Prior research has largely focused on greenfield or synthetic tasks, leaving a gap in structured workflows for complex, context-heavy environments. This paper introduces the Discover-Define-Deliver (D3) Framework, a disciplined LLM-assisted workflow that combines role-separated prompting strategies with applied best practices for navigating ambiguity in brownfield systems. The framework incorporates a dual-agent prompting architecture in which a Builder model generates candidate outputs and a Reviewer model provides structured critique to improve reliability. I conducted an exploratory survey study with 52 software practitioners who applied the D3 workflow to real-world engineering tasks such as legacy system exploration, documentation reconstruction, and architectural refactoring. Respondents reported perceived improvements in task clarity, documentation quality, and cognitive load, along with self-estimated productivity gains. In this exploratory study, participants reported a weighted average productivity improvement of 26.9%, reduced cognitive load for approximately 77% of participants, and reduced rework for 83% during the Define phase. As these findings are self-reported and not derived from controlled experiments, they should be interpreted as preliminary evidence of practitioner sentiment rather than causal effects. The results highlight both the potential and limitations of structured LLM workflows for legacy engineering systems and motivate future controlled evaluations.
涉及遗留系统、不完整的文档以及支离破碎的架构知识的棕地工程工作，对于大型语言模型（LLMs）的有效利用提出了独特的挑战。此前的研究主要集中在绿地项目或合成任务上，忽略了复杂且具有高度上下文背景环境中结构化工作流程的需求。本文介绍了一种名为“发现-定义-交付”(D3)框架的工作流方法，该方法结合了角色分离的提示策略和最佳实践应用，用于在棕地系统中导航模糊性。该框架采用了一个双代理提示架构，在此架构下，一个构建器模型生成候选输出，而另一个审查者模型则提供结构化的批评以提高可靠性。 通过一项探索性的调查研究，52名软件从业人员使用D3工作流处理了诸如遗留系统的探索、文档重建以及架构重构等现实世界中的工程任务。受访者报告称，在任务清晰度、文档质量和认知负荷方面均有感知上的改进，并且自估生产效率有所提升。在这项初步研究中，参与者报告的加权平均生产力提高了26.9%，约77%的参与者的认知负担减少，定义阶段期间83%的参与者减少了返工。 鉴于这些发现是自我报告的而非来自受控实验的结果，因此应将它们视为从业者情感的初步证据，而不是因果效应。该结果强调了结构化LLM工作流程在遗留工程系统中的潜力和局限性，并为未来的受控评估提供了动力。
https://arxiv.org/abs/2512.01155
Large language models (LLMs) play an increasingly important role in finan- cial markets analysis by capturing signals from complex and heterogeneous textual data sources, such as tweets, news articles, reports, and mi