ViTOED is a new dataset for target‑oriented emotion detection in Vietnamese social media, containing 10,985 user comments and 21,244 manually annotated opinion quadruples (source, target, expression, polarity). The dataset uncovers Vietnamese‑specific linguistic phenomena such as implicit sources and targets and vocabulary ambiguities, and it serves as a benchmark for evaluating Vietnamese pre‑trained language models. A baseline using structured sentiment graphs shows that span detection and relation extraction remain challenging, indicating significant room for improvement in Vietnamese target‑oriented emotion detection tasks.
By Chanh Vo, Son T. Luu, Ngan Luu-Thuy Nguyen
arXiv:2309.15670v3 Announce Type: replace
Abstract: In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh m...
By Sumit Kumar Banshal, Sajal Das, Shumaiya Akter Shammi, Narayan Ranjan Chakraborty, Vedika Gupta, Mousumi Karmakar
arXiv:2608. 10810v1 Announce Type: cross Abstract: Emotion understanding in discourse requires reasoning beyond surface sentiment because speakers often convey affect through indirect, implicit, polite, ironic, or deliberately mismatched expressions.
By Zhenyan Zheng, Yunyao Zhang, Junxi Sheng, Junqing Yu, Zikai Song
arXiv:2607. 05259v1 Announce Type: cross Abstract: Sentiment analysis has been a primary domain under Natural Language Processing (NLP) from its inception as it plays a vital role in both real-world and research applications.
By Lakshani Galwatta, Nisansa de Silva, Sarangi Aththanayake, Adithya Galwatta
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:2608.30425v1 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
arXiv:2608.21369v1 Announce Type: cross
Abstract: Nigerian Pidgin is one of Africa's most widely spoken languages, yet remains severely underrepresented in language model evaluation. Existing benchma...
By Stephanie Okoye
The paper introduces BARRAC, a method that adapts an English aspect‑based sentiment analysis framework for Arabic dialect classification tasks. It replaces English consumer‑review attribute pools with Arabic linguistic markers for sentiment, sarcasm, and dialect identification, and swaps noisy self‑training for a two‑stage training process. Evaluated on five Arabic dialect datasets, BARRAC achieves a mean macro‑F1 of 63.93%, surpassing the best few‑label state‑of‑the‑art by 3% and outperforming GPT‑4o on four of the five tasks, while error analysis highlights remaining challenges.
By Ali Almutairi, Gelareh Mohammadi, Imran Razzak, Aditya Joshi
arXiv:2609.16393v1 Announce Type: new
Abstract: We introduce ParsHate, a manually annotated dataset of 10,000 Persian tweets spanning 2013-2022, representing the first decade-long benchmark for hate...
By Zahra Bokaei, Walid Magdy, Bonnie Webber
arXiv:2604. 00819v2 Announce Type: replace-cross Abstract: Understanding emotions in natural language is inherently a multi-dimensional reasoning problem, where multiple affective signals interact through context, interpersonal relations, and situational cues.
By Hemanth Kotaprolu, Kishan Maharaj, Raey Zhao, Abhijit Mishra, Pushpak Bhattacharyya
arXiv:2606. 29614v1 Announce Type: cross Abstract: This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models.
By Sercan Karaka\c{s}, Yusuf \c{S}im\c{s}ek
arXiv:2609.05806v1 Announce Type: new
Abstract: Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide...
By Amir Ben Khalifa, Fanny Bezancon, Amine Trabelsi, Bessam Abdulrazak