The paper introduces KESA, a knowledge‑enhanced approach for sentence‑level sentiment analysis that incorporates sentiment knowledge through two auxiliary tasks: sentiment word cloze and conditional sentiment prediction. These tasks use prior sentiment polarity to guide the selection of sentiment words and the prediction of overall sentiment, respectively, and explore label combination methods to unify multiple label types. Experiments show that KESA consistently outperforms pre‑trained models and complements existing knowledge‑enhanced post‑training methods.
By Qinghua Zhao, Shuai Ma, Shuo Ren
arXiv:2608. 20019v1 Announce Type: new Abstract: Incomplete multimodal sentiment analysis has garnered significant attention in recent years.
By Kaixin Xu, NaiJin Liu, Yulin Kang, Tangyue Jin, Zixuan Yu, Wenxi Zhao, Yibei Liu, Qianle Zhang, Yangyang Wu, Mengying Zhu, Meng Xi
The paper introduces DRIFT, a method that linearizes dependency parse trees and injects them into large language model prompts to improve Aspect Sentiment Quadruple Prediction (ASQP). By providing explicit relational context, DRIFT enables the model to reason over aspect‑opinion dependencies rather than relying on token proximity. Experiments on SemEval Rest15, Rest16, and ACOS show that DRIFT outperforms other generative ASQP methods, achieving 54.47% and 64.10% F1 on Rest15 and Rest16, and demonstrating consistent gains across short- and medium‑distance aspect‑opinion configurations, while also revealing that exact‑match metrics underestimate semantically correct predictions.
By Shima Esfandyari, Seywan Soleymanbaigi, Fardin Akhlaghian Tab, Fatemeh Daneshfar, Mourad Oussalah
The paper presents a method for structured sentiment analysis that constructs a fine‑grained sentiment graph with nodes for sentiment holders, targets, and expressions, and arcs for their relationships. It reframes the task as dependency graph parsing and solves it via sequence labeling, using linearized graph encodings to assign a label to each word. Experiments on seven datasets across five languages demonstrate that this approach achieves performance competitive with more complex single‑model methods.
By Muhammad Imran, Ana Ezquerro, Carlos G\'omez-Rodr\'iguez, Anders S{\o}gaard, David Vilares
The paper proposes using multilingual sentiment lexicons to steer language models for sentiment control, especially in low-resource languages. It finds that aggregated multilingual lexicons outperform English-derived steering vectors for mid- and low-resource languages, while monolingual lexicons are still best for languages the model was pre-trained on. The study also explains how representational density, pre-training priors, and architectural biases affect steering effectiveness, noting that large English vectors can cause linguistic collapse in low-resource settings but this is mitigated by norm-dampening in multilingual aggregation.
By Lucinda Laurent, Muhammad Dehan Al Kautsar, Alfan Farizki Wicaksono, Fajri Koto
arXiv:2608.30425v1 Announce Type: new
Abstract: Cross-lingual aspect-based sentiment analysis (ABSA) transfers knowledge from a source language with annotated data to a target language, enabling fine...
By Jakub \v{S}m\'{i}d, Pavel P\v{r}ib\'{a}\v{n}, Pavel Kr\'{a}l
I$^2$-KAN is a new multimodal sentiment analysis framework that separates intra‑modal and pairwise inter‑modal information into distinct pathways. Each pathway is refined by a Task‑Decoupled Linear‑Gated Denoising Bottleneck and fused using a structured Kolmogorov‑Arnold Network, enabling explicit pathway associations and multi‑level interpretation. Experiments on CMU‑MOSI, CMU‑MOSEI, and CH‑SIMS v2 show strong performance and reveal that a modality’s standalone predictive power does not always reflect its interaction‑conditioned utility.
By Xiaoting Xie, Feng Qiao, Xianwei Zhang, Hailong Chen, Kaiwen Zheng, Joemon M. Jose, Xuri Ge, Yujun Li
FederatedBytes is a small language model that processes text and multimodal data at the byte level, eliminating the need for tokenization. It uses a linear attention mechanism to keep memory and computational demands low, making it suitable for federated learning scenarios. Experiments on multilingual sentiment analysis tasks show that FederatedBytes outperforms models like ByT5 and LSTM, achieving higher accuracy while reducing memory and communication overheads.
By Ali Sarabadani, Massimo Piccardi, Mehran Abolhasan, Farzad Tofigh, Ian Zhou, Justin Lipman
SemMSA introduces a latent semantic‑aided framework for multimodal sentiment analysis that leverages large language models to generate rich sentiment‑relevant semantics. The method employs Cross‑modal Semantic Refinement (CSR) to fuse visual, acoustic, and language features in a frozen LLM embedding space, and Cross‑modal Spectral Alignment (CSA) to align these refined semantics with all modalities via spectral enhancement of kernel Gram matrices. Experiments on SIMS, MOSI, and MOSEI benchmarks show that SemMSA achieves state‑of‑the‑art performance.
By Wenhao Li, Zhibin Wu, Chong Xiao, Qiangchang Wang
arXiv:2606. 03334v1 Announce Type: cross Abstract: Our submission presented in this paper is for SemEval-2026 Task 9: Multilingual Text Classification Challenge - Polarization Detection and it covers all three subtasks: (1) binary polarization detection, (2) polarization type classification and (3) polarization manifestation identification.
By Pritam Kadasi, Anuj Tiwari, Mayank Singh
arXiv:2607. 06611v1 Announce Type: cross Abstract: Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered words.
By Andrei-George Durdun, Victor Constantinescu, Radu Tudor Ionescu
The paper introduces REFLECT, a role-aware transformer that integrates Semantic Role Labeling (SRL) configurations into self‑attention to better detect sentiment, emotion, politeness, and formality (SEPF) in dialogue. By fusing role-aware representations with contextual embeddings through a learned gate, REFLECT achieves significant improvements over BERT baselines on emotion, politeness, and formality tasks, especially for low‑frequency and complex cases, while sentiment gains are minimal. The approach also enhances interpretability and generalizes to real‑world mental‑health data.
By Sangpil Youm, Annabel Stocks Natalias, Hayden Heninger, Leah Jones, Xiaoyang Jin, Xiaoya Zhang, Bonnie Dorr