Sentiment_Classification
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LLM Based Sentiment Classification From Bangladesh E-Commerce Reviews
2025-09-30 16:46:09Sumaiya TabassumAbstract
Sentiment analysis is an essential part of text analysis, which is a larger field that includes determining and evaluating the author's emotional state. This method is essential since it makes it easier to comprehend consumers' feelings, viewpoints, and preferences holistically. The introduction of large language models (LLMs), such as Llama, has greatly increased the availability of cutting-edge model applications, such as sentiment analysis. However, accurate sentiment analysis is hampered by the intricacy of written language and the diversity of languages used in evaluations. The viability of using transformer-based BERT models and other LLMs for sentiment analysis from Bangladesh e commerce reviews is investigated in this paper. A subset of 4000 samples from the original dataset of Bangla and English customer reviews was utilized to fine-tune the model. The fine tuned Llama-3.1-8B model outperformed other fine-tuned models, including Phi-3.5-mini-instruct, Mistral-7B-v0.1, DistilBERT-multilingual, mBERT, and XLM-R-base, with an overall accuracy, precision, recall, and F1 score of 95.5%, 93%, 88%, 90%. The study emphasizes how parameter efficient fine-tuning methods (LoRA and PEFT) can lower computational overhead and make it appropriate for contexts with limited resources. The results show how LLMs can
Abstract (translated)
情感分析是文本分析的一个重要组成部分，而文本分析则涵盖了确定和评估作者的情感状态。这种方法至关重要，因为它使得更全面地理解消费者的情绪、观点和偏好变得更容易。大型语言模型（LLMs）的引入，如Llama，极大地提高了尖端模型应用的可用性，包括情感分析。然而，书面语言的复杂性和评价中所使用语言的多样性阻碍了准确的情感分析。本文研究了在孟加拉国电子商务评论中使用基于变压器的BERT模型和其他大型语言模型进行情感分析的可能性。我们从包含孟加拉语和英语客户评论的原始数据集中选取了一部分共4000个样本，用于微调这些模型。微调后的Llama-3.1-8B模型在整体准确率、精确度、召回率以及F1分数上均优于其他微调过的模型（包括Phi-3.5-mini-instruct, Mistral-7B-v0.1, DistilBERT-multilingual, mBERT和XLM-R-base），分别达到了95.5%，93%，88%和90%。研究强调了参数高效的微调方法（如LoRA和PEFT）如何能够降低计算开销，并使之适应资源有限的环境。实验结果表明，大型语言模型可以有效应用于情感分析任务中，特别是在多语言场景下展现出强大的性能潜力。 总结： 该研究展示了在孟加拉国电子商务评论的情感分析领域使用大规模预训练模型（如Llama-3.1-8B）进行微调的有效性，并且通过实验验证了这些方法的参数效率优势。此外，结果还表明，大型语言模型能够提供更准确和全面的理解，有助于更好地服务于资源受限的情境下的应用需求。
URL
https://arxiv.org/abs/2510.01276
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Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment Analysis: A Comparative Study of Domain Adaptation and Fine-Tuning Strategies
2025-09-20 19:32:16Salha Alyami, Amani Jamal, Areej AlhothaliAbstract
Aspect-based sentiment analysis (ABSA) in natural language processing enables organizations to understand customer opinions on specific product aspects. While deep learning models are widely used for English ABSA, their application in Arabic is limited due to the scarcity of labeled data. Researchers have attempted to tackle this issue by using pre-trained contextualized language models such as BERT. However, these models are often based on fact-based data, which can introduce bias in domain-specific tasks like ABSA. To our knowledge, no studies have applied adaptive pre-training with Arabic contextualized models for ABSA. This research proposes a novel approach using domain-adaptive pre-training for aspect-sentiment classification (ASC) and opinion target expression (OTE) extraction. We examine fine-tuning strategies - feature extraction, full fine-tuning, and adapter-based methods - to enhance performance and efficiency, utilizing multiple adaptation corpora and contextualized models. Our results show that in-domain adaptive pre-training yields modest improvements. Adapter-based fine-tuning is a computationally efficient method that achieves competitive results. However, error analyses reveal issues with model predictions and dataset labeling. In ASC, common problems include incorrect sentiment labeling, misinterpretation of contrastive markers, positivity bias for early terms, and challenges with conflicting opinions and subword tokenization. For OTE, issues involve mislabeling targets, confusion over syntactic roles, difficulty with multi-word expressions, and reliance on shallow heuristics. These findings underscore the need for syntax- and semantics-aware models, such as graph convolutional networks, to more effectively capture long-distance relations and complex aspect-based opinion alignments.
Abstract (translated)
在自然语言处理领域，基于方面的情感分析（ABSA）使组织能够理解客户对产品特定方面的意见。虽然深度学习模型广泛用于英语的ABSA任务，但由于缺乏标注数据，在阿拉伯语中的应用却受到限制。研究人员尝试通过使用像BERT这样的预训练上下文化语言模型来解决这一问题。然而，这些基于事实的数据的模型在领域特定任务如ABSA中可能引入偏差。据我们所知，尚未有研究将自适应预训练与阿拉伯语上下文化模型应用于ABSA。 这项研究提出了一种新颖的方法，使用领域自适应预训练来进行方面情感分类（ASC）和意见目标表达（OTE）提取。我们探讨了三种微调策略：特征抽取、全量微调以及基于适配器的方法，以提高性能和效率，并利用多种适应语料库和上下文化模型进行测试。我们的结果显示，在领域内自适应预训练可获得适度改进。基于适配器的微调是一种计算效率高的方法，能够取得竞争性的结果。然而，错误分析揭示了与模型预测及数据集标注有关的问题。 在ASC中，常见问题包括情感标签不正确、对比标志误读、早期术语积极偏见以及对立意见和子词分词的挑战。对于OTE而言，则涉及目标标记不正确、句法角色混淆、多词表达困难以及对浅层启发式方法依赖等问题。这些发现强调了使用语法和语义感知模型（如图卷积网络）以更有效地捕捉长距离关系及复杂的基于方面意见对齐的重要性。
URL
https://arxiv.org/abs/2509.16788
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Latent Traits and Cross-Task Transfer: Deconstructing Dataset Interactions in LLM Fine-tuning
2025-09-17 01:45:42Shambhavi Krishna, Atharva Naik, Chaitali Agarwal, Sudharshan Govindan, Taesung Lee, Haw-Shiuan ChangAbstract
Large language models are increasingly deployed across diverse applications. This often includes tasks LLMs have not encountered during training. This implies that enumerating and obtaining the high-quality training data for all tasks is infeasible. Thus, we often need to rely on transfer learning using datasets with different characteristics, and anticipate out-of-distribution requests. Motivated by this practical need, we propose an analysis framework, building a transfer learning matrix and dimensionality reduction, to dissect these cross-task interactions. We train and analyze 10 models to identify latent abilities (e.g., Reasoning, Sentiment Classification, NLU, Arithmetic) and discover the side effects of the transfer learning. Our findings reveal that performance improvements often defy explanations based on surface-level dataset similarity or source data quality. Instead, hidden statistical factors of the source dataset, such as class distribution and generation length proclivities, alongside specific linguistic features, are actually more influential. This work offers insights into the complex dynamics of transfer learning, paving the way for more predictable and effective LLM adaptation.
Abstract (translated)
大型语言模型在各种应用中越来越广泛地部署。这通常包括那些在训练过程中未遇到的任务。这意味着为所有任务枚举并获取高质量的训练数据是不可行的。因此，我们常常需要依赖于使用具有不同特征的数据集进行迁移学习，并且要预料到分布外请求的情况。出于这一实际需求，我们提出了一种分析框架，构建一个转移学习矩阵和维度缩减，以剖析这些跨任务交互。通过训练并分析10个模型来识别潜在的能力（如推理、情感分类、自然语言理解、算术），我们发现迁移学习的副作用。我们的研究结果表明，性能提升往往无法用基于表面数据集相似性或源数据质量的解释来充分说明。相反，源数据集中隐藏的统计因素，例如类别分布和生成长度倾向，以及特定的语言特征实际上更具影响力。这项工作提供了对迁移学习复杂动态的理解，并为更可预测且有效的大型语言模型适应铺平了道路。
URL
https://arxiv.org/abs/2509.13624
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Analogy-Driven Financial Chain-of-Thought : A Prompting Approach for Financial Sentiment Analysis
2025-09-16 03:19:26Anmol Singhal Navya SinghalAbstract
Financial news sentiment analysis is crucial for anticipating market movements. With the rise of AI techniques such as Large Language Models (LLMs), which demonstrate strong text understanding capabilities, there has been renewed interest in enhancing these systems. Existing methods, however, often struggle to capture the complex economic context of news and lack transparent reasoning, which undermines their reliability. We propose Analogy-Driven Financial Chain-of-Thought (AD-FCoT), a prompting framework that integrates analogical reasoning with chain-of-thought (CoT) prompting for sentiment prediction on historical financial news. AD-FCoT guides LLMs to draw parallels between new events and relevant historical scenarios with known outcomes, embedding these analogies into a structured, step-by-step reasoning chain. To our knowledge, this is among the first approaches to explicitly combine analogical examples with CoT reasoning in finance. Operating purely through prompting, AD-FCoT requires no additional training data or fine-tuning and leverages the model's internal financial knowledge to generate rationales that mirror human analytical reasoning. Experiments on thousands of news articles show that AD-FCoT outperforms strong baselines in sentiment classification accuracy and achieves substantially higher correlation with market returns. Its generated explanations also align with domain expertise, providing interpretable insights suitable for real-world financial analysis.
Abstract (translated)
财务新闻情绪分析对于预测市场走势至关重要。随着人工智能技术，如大型语言模型（LLMs）的兴起，这些技术展现出强大的文本理解能力，增强了改进此类系统的兴趣。然而，现有的方法往往难以捕捉复杂的经济背景，并且缺乏透明的推理过程，这削弱了它们的可靠性。我们提出了一个基于类比驱动的金融链式思维（AD-FCoT）框架，该框架将类比推理与链式思考（CoT）提示结合在一起，用于对历史财务新闻的情绪预测。通过引导大型语言模型在新事件和相关的历史场景之间建立联系，并利用这些历史场景已知的结果构建结构化的、逐步的推理链条，AD-FCoT能够提升情绪分析的准确性。 据我们所知，这种方法是首个明确将类比示例与CoT推理相结合应用于金融领域的尝试。AD-FCoT完全依靠提示操作，并不需要额外的数据训练或微调，而是利用模型内部的财务知识生成类似于人类分析师的解释性理由。实验结果表明，在对数千篇新闻文章进行分析后，AD-FCoT在情绪分类准确性方面超越了强大的基准方法，并且与市场收益的相关度显著提高。此外，其生成的解释也符合领域专家的知识，提供了适合实际金融分析的可解释见解。 该框架不仅提高了财务新闻情绪预测的准确性和透明性，而且还增强了模型输出结果的应用价值，为金融市场投资者和分析师提供更加可靠的工具和支持。
URL
https://arxiv.org/abs/2509.12611
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Dynamic Span Interaction and Graph-Aware Memory for Entity-Level Sentiment Classification
2025-09-15 05:47:57Md. Mithun Hossain, Sanjara, Md. Shakil Hossain, Sudipto ChakiAbstract
Entity-level sentiment classification involves identifying the sentiment polarity linked to specific entities within text. This task poses several challenges: effectively modeling the subtle and complex interactions between entities and their surrounding sentiment expressions; capturing dependencies that may span across sentences; and ensuring consistent sentiment predictions for multiple mentions of the same entity through coreference resolution. Additionally, linguistic phenomena such as negation, ambiguity, and overlapping opinions further complicate the analysis. These complexities make entity-level sentiment classification a difficult problem, especially in real-world, noisy textual data. To address these issues, we propose SpanEIT, a novel framework integrating dynamic span interaction and graph-aware memory mechanisms for enhanced entity-sentiment relational modeling. SpanEIT builds span-based representations for entities and candidate sentiment phrases, employs bidirectional attention for fine-grained interactions, and uses a graph attention network to capture syntactic and co-occurrence relations. A coreference-aware memory module ensures entity-level consistency across documents. Experiments on FSAD, BARU, and IMDB datasets show SpanEIT outperforms state-of-the-art transformer and hybrid baselines in accuracy and F1 scores. Ablation and interpretability analyses validate the effectiveness of our approach, underscoring its potential for fine-grained sentiment analysis in applications like social media monitoring and customer feedback analysis.
Abstract (translated)
实体级别的情感分类涉及识别文本中特定实体所关联的情感极性。这一任务面临几个挑战：有效建模实体与其周围情感表达之间的细微和复杂交互；捕捉可能跨越句子的情感依赖关系；以及通过共指解析确保同一实体的多次提及具有一致的情感预测。此外，语言现象如否定、歧义及重叠意见进一步增加了分析难度。这些复杂性使得实体级别的文本情感分类成为一个难题，特别是在现实世界中的嘈杂文本数据中更为明显。 为了应对这些问题，我们提出了一种名为SpanEIT的新框架，该框架集成了动态跨度互动和图感知内存机制，以增强对实体-情感关系的建模。SpanEIT构建了基于跨度的实体及候选情感短语表示，并采用双向注意力机制来实现细粒度交互，同时使用图形注意力网络捕获句法和共现关系。一个能够识别共指关系的记忆模块确保了文档中同一实体的情感一致性。 在FSAD、BARU和IMDB数据集上的实验表明，SpanEIT在准确率和F1分数上优于现有的变压器和混合基准模型。消融分析及可解释性研究验证了我们方法的有效性，并强调其在社交网络监控与客户反馈分析等应用中进行细粒度情感分析的潜力。
URL
https://arxiv.org/abs/2509.11604
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A Transformer-Based Cross-Platform Analysis of Public Discourse on the 15-Minute City Paradigm
2025-09-14 21:36:24Gaurab Chhetri, Darrell Anderson, Boniphace Kutela, Subasish DasAbstract
This study presents the first multi-platform sentiment analysis of public opinion on the 15-minute city concept across Twitter, Reddit, and news media. Using compressed transformer models and Llama-3-8B for annotation, we classify sentiment across heterogeneous text domains. Our pipeline handles long-form and short-form text, supports consistent annotation, and enables reproducible evaluation. We benchmark five models (DistilRoBERTa, DistilBERT, MiniLM, ELECTRA, TinyBERT) using stratified 5-fold cross-validation, reporting F1-score, AUC, and training time. DistilRoBERTa achieved the highest F1 (0.8292), TinyBERT the best efficiency, and MiniLM the best cross-platform consistency. Results show News data yields inflated performance due to class imbalance, Reddit suffers from summarization loss, and Twitter offers moderate challenge. Compressed models perform competitively, challenging assumptions that larger models are necessary. We identify platform-specific trade-offs and propose directions for scalable, real-world sentiment classification in urban planning discourse.
Abstract (translated)
本研究首次对“15分钟城市”概念在Twitter、Reddit和新闻媒体上的公众意见进行了多平台情感分析。我们使用压缩的Transformer模型和Llama-3-8B进行注释，能够跨异构文本领域分类情感。我们的工作流程处理长格式和短格式文本，支持一致的注释，并使可重复性评估成为可能。通过分层五折交叉验证对五个模型（DistilRoBERTa、DistilBERT、MiniLM、ELECTRA、TinyBERT）进行基准测试，报告F1分数、AUC值和训练时间。其中，DistilRoBERTa取得了最高的F1得分（0.8292），而TinyBERT则在效率方面表现最佳，MiniLM表现出最佳的跨平台一致性。结果表明，新闻数据由于类别不平衡而导致性能虚高，Reddit因总结损失而受到影响，Twitter提供了适度的挑战性。压缩模型的表现竞争激烈，挑战了更大规模模型必不可少的假设。我们识别出各平台特定的权衡，并提出方向以在城市规划讨论中实现可扩展且现实的情感分类。
URL
https://arxiv.org/abs/2509.11443
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Target-oriented Multimodal Sentiment Classification with Counterfactual-enhanced Debiasing
2025-09-11 05:40:53Zhiyue Liu, Fanrong Ma, Xin LingAbstract
Target-oriented multimodal sentiment classification seeks to predict sentiment polarity for specific targets from image-text pairs. While existing works achieve competitive performance, they often over-rely on textual content and fail to consider dataset biases, in particular word-level contextual biases. This leads to spurious correlations between text features and output labels, impairing classification accuracy. In this paper, we introduce a novel counterfactual-enhanced debiasing framework to reduce such spurious correlations. Our framework incorporates a counterfactual data augmentation strategy that minimally alters sentiment-related causal features, generating detail-matched image-text samples to guide the model's attention toward content tied to sentiment. Furthermore, for learning robust features from counterfactual data and prompting model decisions, we introduce an adaptive debiasing contrastive learning mechanism, which effectively mitigates the influence of biased words. Experimental results on several benchmark datasets show that our proposed method outperforms state-of-the-art baselines.
Abstract (translated)
以目标为导向的多模态情感分类旨在从图像-文本对中预测特定目标的情感极性。尽管现有研究已经取得了竞争性的性能，但它们往往过度依赖于文本内容，并且未能考虑数据集中的偏差，尤其是词级别的上下文偏见。这导致了文本特征与输出标签之间的虚假相关性，从而损害了分类的准确性。在本文中，我们引入了一种新颖的反事实增强去偏框架，以减少此类虚假相关性。我们的框架结合了一个反事实数据扩充策略，该策略最小程度地修改情感相关的因果特征，并生成细节匹配的图像-文本样本，引导模型关注与情感内容相关的内容。此外，为了从反事实数据中学习稳健的功能并提示模型决策，我们引入了一种自适应去偏对比学习机制，这有效地减轻了有偏差单词的影响。在几个基准数据集上的实验结果表明，我们提出的方法优于现有的最先进基线方法。
URL
https://arxiv.org/abs/2509.09160
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Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts
2025-09-05 10:08:14Julius Neumann, Robert Lange, Yuni Susanti, Michael F\"arberAbstract
Sentiment classification in short text datasets faces significant challenges such as class imbalance, limited training samples, and the inherent subjectivity of sentiment labels -- issues that are further intensified by the limited context in short texts. These factors make it difficult to resolve ambiguity and exacerbate data sparsity, hindering effective learning. In this paper, we evaluate the effectiveness of small Transformer-based models (i.e., BERT and RoBERTa, with fewer than 1 billion parameters) for multi-label sentiment classification, with a particular focus on short-text settings. Specifically, we evaluated three key factors influencing model performance: (1) continued domain-specific pre-training, (2) data augmentation using automatically generated examples, specifically generative data augmentation, and (3) architectural variations of the classification head. Our experiment results show that data augmentation improves classification performance, while continued pre-training on augmented datasets can introduce noise rather than boost accuracy. Furthermore, we confirm that modifications to the classification head yield only marginal benefits. These findings provide practical guidance for optimizing BERT-based models in resource-constrained settings and refining strategies for sentiment classification in short-text datasets.
Abstract (translated)
在短文本数据集上进行情感分类面临诸如类别不平衡、训练样本有限以及情感标签主观性强等重大挑战，这些问题因短文中上下文信息的局限性而变得更加复杂。这些因素使得解决模棱两可的问题变得困难，并加剧了数据稀疏性，从而阻碍了有效的学习过程。在这篇论文中，我们评估了基于小规模Transformer模型（即参数数量少于10亿个的BERT和RoBERTa）在多标签情感分类任务中的有效性，特别是在处理短文本场景时的效果。具体来说，我们重点研究了影响模型性能的三个关键因素：(1) 在特定领域内进行持续预训练；(2) 使用自动生成样例的数据增强技术（特别是利用生成式数据增强方法）；以及 (3) 分类头的设计变体。 我们的实验结果显示，在短文本情感分类中，数据增强可以显著提高模型的分类性能。然而，继续在扩充后的数据集上进行预训练可能会引入噪声而不是提升准确率。此外，我们确认对分类头部所做的修改仅带来了边际效益。 这些发现为优化资源受限环境下的BERT及其衍生模型提供了实用指导，并有助于完善针对短文本情感分类策略的方法论。
URL
https://arxiv.org/abs/2509.04982
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SentiMM: A Multimodal Multi-Agent Framework for Sentiment Analysis in Social Media
2025-08-25 15:17:53Xilai Xu, Zilin Zhao, Chengye Song, Zining Wang, Jinhe Qiang, Jiongrui Yan, Yuhuai LinAbstract
With the increasing prevalence of multimodal content on social media, sentiment analysis faces significant challenges in effectively processing heterogeneous data and recognizing multi-label emotions. Existing methods often lack effective cross-modal fusion and external knowledge integration. We propose SentiMM, a novel multi-agent framework designed to systematically address these challenges. SentiMM processes text and visual inputs through specialized agents, fuses multimodal features, enriches context via knowledge retrieval, and aggregates results for final sentiment classification. We also introduce SentiMMD, a large-scale multimodal dataset with seven fine-grained sentiment categories. Extensive experiments demonstrate that SentiMM achieves superior performance compared to state-of-the-art baselines, validating the effectiveness of our structured approach.
Abstract (translated)
随着社交媒体上多模态内容的日益普及，情感分析面临着在处理异质数据和识别多标签情绪方面的重要挑战。现有的方法往往缺乏有效的跨模式融合和外部知识整合能力。为此，我们提出了SentiMM，这是一个旨在系统地解决这些问题的新颖多代理框架。SentiMM通过专门的代理来处理文本和视觉输入，融合多模态特征，并通过知识检索丰富上下文信息，最后聚合结果以进行最终的情感分类。 为了支持这一研究，我们还引入了SentiMMD，这是一个包含七个细粒度情感类别的大规模多模态数据集。广泛的实验表明，与最先进的基线方法相比，SentiMM在性能上表现出显著优势，这验证了我们的结构化方法的有效性。
URL
https://arxiv.org/abs/2508.18108
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CausalSent: Interpretable Sentiment Classification with RieszNet
2025-08-25 00:56:51Daniel Frees, Martin PollackAbstract
Despite the overwhelming performance improvements offered by recent natural language procesing (NLP) models, the decisions made by these models are largely a black box. Towards closing this gap, the field of causal NLP combines causal inference literature with modern NLP models to elucidate causal effects of text features. We replicate and extend Bansal et al's work on regularizing text classifiers to adhere to estimated effects, focusing instead on model interpretability. Specifically, we focus on developing a two-headed RieszNet-based neural network architecture which achieves better treatment effect estimation accuracy. Our framework, CausalSent, accurately predicts treatment effects in semi-synthetic IMDB movie reviews, reducing MAE of effect estimates by 2-3x compared to Bansal et al's MAE on synthetic Civil Comments data. With an ensemble of validated models, we perform an observational case study on the causal effect of the word "love" in IMDB movie reviews, finding that the presence of the word "love" causes a +2.9% increase in the probability of a positive sentiment.
Abstract (translated)
尽管最近的自然语言处理（NLP）模型提供了显著的性能改进，但这些模型做出决策的过程仍然很大程度上是一个黑盒。为了填补这一空白，因果NLP领域将因果推理文献与现代NLP模型结合在一起，以阐明文本特征的因果效应。我们复制并扩展了Bansal等人关于通过正则化使文本分类器遵循估计效果的工作，并侧重于提高模型可解释性。具体而言，我们专注于开发一种基于RieszNet的双头神经网络架构，该架构在治疗效果估计准确性方面表现更好。 我们的框架CausalSent能够在半合成IMDB电影评论数据上准确预测治疗效应，与Bansal等人在合成Civil Comments数据上的平均绝对误差（MAE）相比，我们对效果估计的MAE减少了2到3倍。通过使用一组经过验证的模型进行集合分析，我们在IMDB电影评论中进行了观察性案例研究，以探究单词“love”的因果效应，发现该词的存在使正面情感的概率增加了+2.9%。
URL
https://arxiv.org/abs/2508.17576
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Do Multimodal LLMs See Sentiment?
2025-08-23 02:11:46Neemias B. da Silva, John Harrison, Rodrigo Minetto, Myriam R. Delgado, Bogdan T. Nassu, Thiago H. SilvaAbstract
Understanding how visual content communicates sentiment is critical in an era where online interaction is increasingly dominated by this kind of media on social platforms. However, this remains a challenging problem, as sentiment perception is closely tied to complex, scene-level semantics. In this paper, we propose an original framework, MLLMsent, to investigate the sentiment reasoning capabilities of Multimodal Large Language Models (MLLMs) through three perspectives: (1) using those MLLMs for direct sentiment classification from images; (2) associating them with pre-trained LLMs for sentiment analysis on automatically generated image descriptions; and (3) fine-tuning the LLMs on sentiment-labeled image descriptions. Experiments on a recent and established benchmark demonstrate that our proposal, particularly the fine-tuned approach, achieves state-of-the-art results outperforming Lexicon-, CNN-, and Transformer-based baselines by up to 30.9%, 64.8%, and 42.4%, respectively, across different levels of evaluators' agreement and sentiment polarity categories. Remarkably, in a cross-dataset test, without any training on these new data, our model still outperforms, by up to 8.26%, the best runner-up, which has been trained directly on them. These results highlight the potential of the proposed visual reasoning scheme for advancing affective computing, while also establishing new benchmarks for future research.
Abstract (translated)
理解视觉内容如何传达情感在社交媒体平台上这种媒体形式主导在线互动的时代至关重要。然而，这仍然是一个挑战性问题，因为情感感知与复杂的场景级语义紧密相关。为此，本文提出了一种原创框架MLLMsent，用于通过三种视角来研究多模态大型语言模型（MLLM）的情感推理能力：(1)直接使用这些MLLM从图像进行情感分类；(2)将它们与预训练的语言模型关联起来，在自动产生的图像描述上进行情感分析；以及 (3)在带有情感标签的图像描述上对语言模型进行微调。实验表明，基于最近且已被确立的基准测试，我们的提案——尤其是微调方法——实现了最先进的结果，分别比词典、CNN和Transformer基线方法提高了高达30.9%、64.8% 和 42.4%，在不同级别的评估者共识和情感极性类别中均表现出色。特别值得注意的是，在跨数据集测试中，即使没有在这类新数据上进行任何训练，我们的模型仍然优于最佳竞争对手多达8.26%，后者直接在这些数据上进行了训练。这些结果突显了所提出的视觉推理方案在推进情绪计算方面的潜力，并为未来研究建立了新的基准。
URL
https://arxiv.org/abs/2508.16873
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Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challenges
2025-08-13 05:55:53Jakub \v{S}m\'id, Pavel Kr\'alAbstract
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that focuses on understanding opinions at the aspect level, including sentiment towards specific aspect terms, categories, and opinions. While ABSA research has seen significant progress, much of the focus has been on monolingual settings. Cross-lingual ABSA, which aims to transfer knowledge from resource-rich languages (such as English) to low-resource languages, remains an under-explored area, with no systematic review of the field. This paper aims to fill that gap by providing a comprehensive survey of cross-lingual ABSA. We summarize key ABSA tasks, including aspect term extraction, aspect sentiment classification, and compound tasks involving multiple sentiment elements. Additionally, we review the datasets, modelling paradigms, and cross-lingual transfer methods used to solve these tasks. We also examine how existing work in monolingual and multilingual ABSA, as well as ABSA with LLMs, contributes to the development of cross-lingual ABSA. Finally, we highlight the main challenges and suggest directions for future research to advance cross-lingual ABSA systems.
Abstract (translated)
基于方面的情感分析（ABSA）是一项精细粒度的情感分析任务，专注于在特定层面理解意见，包括对具体方面术语、类别和观点的情感态度。尽管ABSA研究已经取得了显著进展，但大多数的研究重点仍然放在单语环境上。跨语言ABSA旨在将知识从资源丰富的语言（如英语）转移到低资源语言中，这一领域仍然是一个尚未充分探索的区域，并且没有系统的文献回顾。本文旨在填补这一空白，提供一份全面的跨语言ABSA调查报告。我们总结了关键的ABSA任务，包括方面术语提取、方面情感分类以及涉及多个情感元素的复合任务。此外，我们还审查了解决这些任务所使用的数据集、建模范式和跨语言迁移方法。我们还探讨了单语和多语ABSA以及使用大规模语言模型（LLMs）进行ABSA的相关工作如何推动跨语言ABSA的发展。最后，本文强调主要挑战，并提出未来研究的方向，以促进跨语言ABSA系统的发展。
URL
https://arxiv.org/abs/2508.09516
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Prompt-Based Approach for Czech Sentiment Analysis
2025-08-12 05:31:07Jakub \v{S}m\'id, Pavel P\v{r}ib\'a\v{n}Abstract
This paper introduces the first prompt-based methods for aspect-based sentiment analysis and sentiment classification in Czech. We employ the sequence-to-sequence models to solve the aspect-based tasks simultaneously and demonstrate the superiority of our prompt-based approach over traditional fine-tuning. In addition, we conduct zero-shot and few-shot learning experiments for sentiment classification and show that prompting yields significantly better results with limited training examples compared to traditional fine-tuning. We also demonstrate that pre-training on data from the target domain can lead to significant improvements in a zero-shot scenario.
Abstract (translated)
本文介绍了首个用于捷克语基于方面的情感分析和情感分类的提示（prompt）方法。我们采用序列到序列模型同时解决基于方面的任务，并展示了我们的提示方法相对于传统微调方法的优势。此外，我们在情感分类中进行了零样本学习和少量样本学习实验，结果显示，在训练示例有限的情况下，使用提示能够显著优于传统的微调方法。另外，我们也证明了在目标领域的数据上进行预训练可以在零样本场景下带来显著的改进。
URL
https://arxiv.org/abs/2508.08651
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Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation
2025-08-07 10:23:14Haonan Shangguan, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, Ge YuAbstract
The surge in rich multimodal content on social media platforms has greatly advanced Multimodal Sentiment Analysis (MSA), with Large Language Models (LLMs) further accelerating progress in this field. Current approaches primarily leverage the knowledge and reasoning capabilities of parameter-heavy (Multimodal) LLMs for sentiment classification, overlooking autonomous multimodal sentiment reasoning generation in resource-constrained environments. Therefore, we focus on the Resource-Limited Joint Multimodal Sentiment Reasoning and Classification task, JMSRC, which simultaneously performs multimodal sentiment reasoning chain generation and sentiment classification only with a lightweight model. We propose a Multimodal Chain-of-Thought Reasoning Distillation model, MulCoT-RD, designed for JMSRC that employs a "Teacher-Assistant-Student" distillation paradigm to address deployment constraints in resource-limited environments. We first leverage a high-performance Multimodal Large Language Model (MLLM) to generate the initial reasoning dataset and train a medium-sized assistant model with a multi-task learning mechanism. A lightweight student model is jointly trained to perform efficient multimodal sentiment reasoning generation and classification. Extensive experiments on four datasets demonstrate that MulCoT-RD with only 3B parameters achieves strong performance on JMSRC, while exhibiting robust generalization and enhanced interpretability.
Abstract (translated)
社交媒体平台上丰富多模态内容的激增极大地推动了多模态情感分析（Multimodal Sentiment Analysis，MSA）的发展。大型语言模型（Large Language Models，LLMs）进一步加速了这一领域的进步。然而，当前的方法主要依赖于参数量大的（多模态）LLM进行情感分类，而忽视了在资源受限环境中自主生成的多模态情感推理。因此，我们专注于“资源限制下的联合多模态情感推理与分类”任务，简称JMSRC，该任务仅使用轻量级模型同时执行多模态情感推理链生成和情感分类。 为此，我们提出了一种名为Multimodal Chain-of-Thought Reasoning Distillation（MulCoT-RD）的模型，专门针对JMSRC任务设计。此模型采用“教师-助理-学生”蒸馏范式，以解决资源受限环境中的部署约束问题。首先利用高性能多模态大型语言模型（Multimodal Large Language Model, MLLM）生成初始推理数据集，并通过多任务学习机制训练一个中等规模的辅助模型。然后共同训练一个轻量级学生模型，用于执行高效的多模态情感推理生成和分类。 在四个数据集上的广泛实验表明，仅拥有30亿参数的MulCoT-RD在JMSRC任务上表现出色，并展示了强大的泛化能力和增强的可解释性。
URL
https://arxiv.org/abs/2508.05234
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Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification
2025-08-05 07:44:42Lukas P\"atz, Moritz Beyer, Jannik Sp\"ath, Lasse Bohlen, Patrick Zschech, Mathias Kraus, Julian RosenbergerAbstract
This study investigates political discourse in the German parliament, the Bundestag, by analyzing approximately 28,000 parliamentary speeches from the last five years. Two machine learning models for topic and sentiment classification were developed and trained on a manually labeled dataset. The models showed strong classification performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.94 for topic classification (average across topics) and 0.89 for sentiment classification. Both models were applied to assess topic trends and sentiment distributions across political parties and over time. The analysis reveals remarkable relationships between parties and their role in parliament. In particular, a change in style can be observed for parties moving from government to opposition. While ideological positions matter, governing responsibilities also shape discourse. The analysis directly addresses key questions about the evolution of topics, sentiment dynamics, and party-specific discourse strategies in the Bundestag.
Abstract (translated)
这项研究通过对过去五年内约28,000篇德国联邦议院（Bundestag）的议会演讲进行分析，探讨了政治话语。为此开发并训练了两个用于主题和情感分类的机器学习模型，这些模型是在手动标记的数据集上训练出来的。在评估中，这两个模型表现出色：主题分类的受试者操作特征曲线下面积（AUROC）为0.94（平均值），情感分类则为0.89。 这两种模型被用于评估各政党及其随时间变化的主题趋势和情感分布情况。分析结果揭示了党派之间令人瞩目的关系以及他们在议会中的角色作用。尤其是，从执政党转变为反对党的过程中可以看到明显的风格转变。虽然意识形态立场很重要，但治理责任也塑造着政治话语的内容。 这项研究直接回答了关于主题演变、情感动态变化及各政党特定的议会议程策略在联邦议院中如何发展的关键问题。
URL
https://arxiv.org/abs/2508.03181
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EHSAN: Leveraging ChatGPT in a Hybrid Framework for Arabic Aspect-Based Sentiment Analysis in Healthcare
2025-08-04 16:28:58Eman Alamoudi, Ellis SolaimanAbstract
Arabic-language patient feedback remains under-analysed because dialect diversity and scarce aspect-level sentiment labels hinder automated assessment. To address this gap, we introduce EHSAN, a data-centric hybrid pipeline that merges ChatGPT pseudo-labelling with targeted human review to build the first explainable Arabic aspect-based sentiment dataset for healthcare. Each sentence is annotated with an aspect and sentiment label (positive, negative, or neutral), forming a pioneering Arabic dataset aligned with healthcare themes, with ChatGPT-generated rationales provided for each label to enhance transparency. To evaluate the impact of annotation quality on model performance, we created three versions of the training data: a fully supervised set with all labels reviewed by humans, a semi-supervised set with 50% human review, and an unsupervised set with only machine-generated labels. We fine-tuned two transformer models on these datasets for both aspect and sentiment classification. Experimental results show that our Arabic-specific model achieved high accuracy even with minimal human supervision, reflecting only a minor performance drop when using ChatGPT-only labels. Reducing the number of aspect classes notably improved classification metrics across the board. These findings demonstrate an effective, scalable approach to Arabic aspect-based sentiment analysis (SA) in healthcare, combining large language model annotation with human expertise to produce a robust and explainable dataset. Future directions include generalisation across hospitals, prompt refinement, and interpretable data-driven modelling.
Abstract (translated)
阿拉伯语患者反馈的分析仍然不足，主要是因为方言多样性以及情感标签稀缺阻碍了自动化评估。为了填补这一空白，我们引入了一个名为EHSAN的数据中心化混合管道，该系统结合了ChatGPT伪标注和定向人工审核，以构建首个解释性的阿拉伯语基于方面的医疗保健情感数据集。每个句子都用一个方面及其相应的情感标签（正面、负面或中立）进行了注释，形成了与健康主题对齐的开创性阿拉伯语数据集，并且为每一个标签提供了ChatGPT生成的理由，以此来增强透明度。 为了评估标注质量对模型性能的影响，我们创建了训练数据的三个版本：完全监督的数据集（所有标签都由人类审核）、半监督数据集（50%的人工审核）和无监督数据集（仅包含机器生成的标签）。我们在这些数据集上微调了两个转换器模型以进行基于方面的情感分类。实验结果显示，即使在最小人工监督的情况下，我们的阿拉伯语特定模型也达到了高准确率，而使用ChatGPT生成的标签时，其性能仅略有下降。 值得注意的是，在将情感类别减少到更少数量后，所有类别的分类指标都有显著改善。这些发现表明了一种有效的、可扩展的方法来在医疗保健领域进行基于方面的阿拉伯语情感分析（SA），通过结合大型语言模型标注和人类专业知识来生成稳健且解释性高的数据集。未来的方向包括医院间的推广、提示优化以及基于数据的可解释建模方法的发展。
URL
https://arxiv.org/abs/2508.02574
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LatentPrompt: Optimizing Promts in Latent Space
2025-08-04 14:17:29Mateusz Bystro\'nski, Grzegorz Piotrowski, Nitesh V. Chawla, Tomasz KajdanowiczAbstract
Recent advances have shown that optimizing prompts for Large Language Models (LLMs) can significantly improve task performance, yet many optimization techniques rely on heuristics or manual exploration. We present LatentPrompt, a model-agnostic framework for prompt optimization that leverages latent semantic space to automatically generate, evaluate, and refine candidate prompts without requiring hand-crafted rules. Beginning with a set of seed prompts, our method embeds them in a continuous latent space and systematically explores this space to identify prompts that maximize task-specific performance. In a proof-of-concept study on the Financial PhraseBank sentiment classification benchmark, LatentPrompt increased classification accuracy by approximately 3 percent after a single optimization cycle. The framework is broadly applicable, requiring only black-box access to an LLM and an automatic evaluation metric, making it suitable for diverse domains and tasks.
Abstract (translated)
最近的研究表明，为大型语言模型（LLM）优化提示可以显著提升任务性能，但许多优化技术依赖于启发式方法或手动探索。我们提出了一种名为LatentPrompt的框架，这是一种与模型无关的提示优化方法，它利用潜在语义空间自动生成、评估和改进候选提示，并且无需手工定制规则。该方法从一组初始提示开始，将其嵌入到连续的潜在空间中，并系统地在这个空间内进行探索以识别能够最大化特定任务性能的提示。 在金融短语库（Financial PhraseBank）情感分类基准测试的一个概念验证研究中，在经过一轮优化之后，LatentPrompt使分类准确率提高了大约3%。该框架具有广泛的适用性，仅需对LLM和自动评估指标有黑盒访问权限即可使用，因此适用于多种领域和任务。
URL
https://arxiv.org/abs/2508.02452
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TeSent: A Benchmark Dataset for Fairness-aware Explainable Sentiment Classification in Telugu
2025-08-02 20:42:37Vallabhaneni Raj Kumar, Ashwin S, Supriya Manna, Niladri Sett, Cheedella V S N M S Hema Harshitha, Kurakula Harshitha, Anand Kumar Sharma, Basina Deepakraj, Tanuj Sarkar, Bondada Navaneeth Krishna, Samanthapudi ShakeerAbstract
In the Indian subcontinent, Telugu, one of India's six classical languages, is the most widely spoken Dravidian Language. Despite its 96 million speaker base worldwide, Telugu remains underrepresented in the global NLP and Machine Learning landscape, mainly due to lack of high-quality annotated resources. This work introduces TeSent, a comprehensive benchmark dataset for sentiment classification, a key text classification problem, in Telugu. TeSent not only provides ground truth labels for the sentences, but also supplements with provisions for evaluating explainability and fairness, two critical requirements in modern-day machine learning tasks. We scraped Telugu texts covering multiple domains from various social media platforms, news websites and web-blogs to preprocess and generate 26,150 sentences, and developed a custom-built annotation platform and a carefully crafted annotation protocol for collecting the ground truth labels along with their human-annotated rationales. We then fine-tuned several SOTA pre-trained models in two ways: with rationales, and without rationales. Further, we provide a detailed plausibility and faithfulness evaluation suite, which exploits the rationales, for six widely used post-hoc explainers applied on the trained models. Lastly, we curate TeEEC, Equity Evaluation Corpus in Telugu, a corpus to evaluate fairness of Telugu sentiment and emotion related NLP tasks, and provide a fairness evaluation suite for the trained classifier models. Our experimental results suggest that training with rationales may improve model accuracy, reduce bias in models, and make the explainers' output more aligned to human reasoning.
Abstract (translated)
在印度次大陆，泰卢固语（Telugu）是印度六种古典语言之一，也是使用最广泛的德拉威语系语言。尽管其全球使用者数量达到9600万，但由于高质量标注资源的缺乏，泰卢固语在全球自然语言处理和机器学习领域中仍处于相对边缘化的地位。为此，本研究推出了TeSent，一个全面的基准数据集，用于泰卢固语情感分类这一关键文本分类问题的研究。 TeSent不仅提供了句子的真实标签，还补充了评估可解释性和公平性的机制，这两者是现代机器学习任务中的重要需求。我们从各种社交媒体平台、新闻网站和网络博客中抓取了大量的泰卢固语文本，涵盖多个领域，并预处理生成了26,150个句子。同时，我们建立了一个定制的标注平台以及一个精心设计的标注协议来收集这些真实的标签及其人工标注的理由。 接着，我们在两个方面对几个最先进的预训练模型进行了微调：一是带有理由的情况；二是不带理由的情况。此外，我们还提供了一套详细的合理性与忠实性评估工具，这套工具利用了上述理由，并应用于六种广泛使用的后处理解释器，在训练后的模型上进行应用。 最后，为了评估泰卢固语情感和情绪相关的自然语言处理任务的公平性，我们整理出了TeEEC（Telugu Equity Evaluation Corpus），这是一份专门用于评估此类任务中模型公平性的语料库，并且为这些经过训练的分类器提供了一套公平性评估工具。 实验结果表明，使用理由进行训练可以提高模型的准确性，减少模型中的偏见，并使解释器的输出更加符合人类推理。
URL
https://arxiv.org/abs/2508.01486
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Understanding Public Perception of Crime in Bangladesh: A Transformer-Based Approach with Explainability
2025-07-28 18:00:25Fatema Binte Hassan, Md Al Jubair, Mohammad Mehadi Hasan, Tahmid Hossain, S M Mehebubur Rahman Khan Shuvo, Mohammad Shamsul ArefinAbstract
In recent years, social media platforms have become prominent spaces for individuals to express their opinions on ongoing events, including criminal incidents. As a result, public sentiment can shift dynamically over time. This study investigates the evolving public perception of crime-related news by classifying user-generated comments into three categories: positive, negative, and neutral. A newly curated dataset comprising 28,528 Bangla-language social media comments was developed for this purpose. We propose a transformer-based model utilizing the XLM-RoBERTa Base architecture, which achieves a classification accuracy of 97%, outperforming existing state-of-the-art methods in Bangla sentiment analysis. To enhance model interpretability, explainable AI technique is employed to identify the most influential features driving sentiment classification. The results underscore the effectiveness of transformer-based models in processing low-resource languages such as Bengali and demonstrate their potential to extract actionable insights that can support public policy formulation and crime prevention strategies.
Abstract (translated)
近年来，社交媒体平台已成为个人表达对当前事件（包括犯罪事件）看法的重要空间。因此，公众情绪会随着时间的推移而动态变化。本研究通过将用户生成的评论分类为三类：积极、消极和中立，来探讨与犯罪相关的新闻报道中的公众认知演变。为此目的开发了一个新整理的数据集，包含28,528条孟加拉语社交媒体评论。 我们提出了一种基于变压器架构（使用XLM-RoBERTa Base）的模型，在孟加拉语情感分析中实现了97%的分类准确率，超越了现有最先进的方法。为了提高模型可解释性，采用了可解释的人工智能技术来识别驱动情绪分类的关键特征。研究结果强调了基于Transformer的模型在处理包括孟加拉语在内的低资源语言中的有效性，并展示了它们提取可用于支持公共政策制定和犯罪预防策略的实用信息的能力。
URL
https://arxiv.org/abs/2507.21234
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PITA: Preference-Guided Inference-Time Alignment for LLM Post-Training
2025-07-26 21:46:32Sarat Chandra Bobbili, Ujwal Dinesha, Dheeraj Narasimha, Srinivas ShakkottaiAbstract
Inference-time alignment enables large language models (LLMs) to generate outputs aligned with end-user preferences without further training. Recent post-training methods achieve this by using small guidance models to modify token generation during inference. These methods typically optimize a reward function KL-regularized by the original LLM taken as the reference policy. A critical limitation, however, is their dependence on a pre-trained reward model, which requires fitting to human preference feedback--a potentially unstable process. In contrast, we introduce PITA, a novel framework that integrates preference feedback directly into the LLM's token generation, eliminating the need for a reward model. PITA learns a small preference-based guidance policy to modify token probabilities at inference time without LLM fine-tuning, reducing computational cost and bypassing the pre-trained reward model dependency. The problem is framed as identifying an underlying preference distribution, solved through stochastic search and iterative refinement of the preference-based guidance model. We evaluate PITA across diverse tasks, including mathematical reasoning and sentiment classification, demonstrating its effectiveness in aligning LLM outputs with user preferences.
Abstract (translated)
推理时间对齐使大型语言模型（LLM）能够在无需进一步训练的情况下生成与最终用户偏好一致的输出。最近的后期训练方法通过使用小规模引导模型来修改推断过程中的令牌生成，从而实现了这一点。这些方法通常优化一个由原始LLM作为参考策略进行KL正则化的奖励函数。然而，一个关键限制是它们依赖于预训练的奖励模型，该模型需要适应人类偏好的反馈——这是一个可能不稳定的过程。相比之下，我们引入了PITA这一新颖框架，它直接将偏好反馈整合到LLM的令牌生成过程中，从而消除了对奖励模型的需求。PITA学习一个小规模基于偏好的引导策略，在推理时间修改令牌概率而无需对LLM进行微调，这样可以减少计算成本并绕过预训练奖励模型的依赖性。该问题被构架为识别潜在偏好分布的问题，并通过随机搜索和迭代改进基于偏好的引导模型来解决。我们在包括数学推理和情感分类在内的各种任务上评估了PITA，证明了它在将LLM输出与用户偏好对齐方面的有效性。
URL
https://arxiv.org/abs/2507.20067
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