The paper introduces a hybrid BERT‑CNN‑BiLSTM model for simultaneously classifying Bangla news headlines and performing sentiment analysis. Using the BAN‑ABSA dataset of 9,014 headlines, the authors compare two sampling strategies and report headline accuracy up to 81.37 % and sentiment accuracy up to 73.43 %. The proposed framework outperforms baseline models and establishes new state‑of‑the‑art results for Bangla headline classification and sentiment analysis.
By Mirza Raquib, Munazer Montasir Akash, Tawhid Ahmed, Saydul Akbar Murad, Farida Siddiqi Prity, Mohammad Amzad Hossain, Asif Pervez Polok, Nick Rahimi
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:2608.22922v1 Announce Type: new
Abstract: We present HelaBERT, a family of two BERT-based masked language models pre-trained from scratch on approximately 1 billion tokens of Sinhala text sourc...
By Thisen Ekanayake, Nisansa de Silva
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:2606.18389v2 Announce Type: replace
Abstract: Large language models (LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated dat...
By Jan Cegin, Daniil Gurgurov, Yusser Al Ghussin, Simon Ostermann
arXiv:2609.36194v1 Announce Type: new
Abstract: Extracted sentiment directions can vary across samples even when downstream sentiment classification remains accurate. To evaluate direction reproducib...
By Muhammad Abdullahi Said, Abass Oguntade, Elisha Komolafe, Babangida Sani, Fatima Muhammad Adam, Muhammad Sammani Sani
arXiv:2610.10261v1 Announce Type: cross
Abstract: Context: Once defined a taxonomy of stages structuring Machine Learning (ML) pipelines (e.g. Data Preprocessing, Modeling...), extracting these stage...
By Nicolas Lacroix, Frederic Precioso, Mireille Blay-Fornarino, Sebastien Mosser
The paper explores using LLM-generated annotations to train smaller BERT models for literary sentiment analysis. Five BERT variants were fine‑tuned with ordinal regression on 10,889 sentences labeled by Gemma 4 31B on a ten‑point scale, and evaluated on 1,923 human‑annotated sentences from Hemingway’s *The Old Man and the Sea*. BERT‑large achieved a Kendall’s τ of 0.589, close to the teacher’s 0.604, and BERT‑base outperformed the teacher on non‑neutral sentences with an MAE of 0.726 versus 1.116.
By Artem Suslov, Takehiro Hashimoto
The paper introduces a method that adapts Contextual Decomposition for Transformers (CD‑T) to discover neural circuits without relying on clean counterfactuals, using label‑balanced activation means and task‑directional relevance scoring. These circuits are then employed in Circuit‑Targeted Supervised Fine‑Tuning (CT‑SFT), which restricts parameter updates to task‑relevant heads and LayerNorm, leading to competitive performance on low‑resource language adaptation tasks such as NusaX cross‑lingual sentiment transfer. CT‑SFT consistently avoids catastrophic forgetting and preserves source‑language and related‑task performance, offering a more controlled alternative to global fine‑tuning, as further validated on the XNLI benchmark.
By Khumaisa Nur'aini, Ayu Purwarianti, Alham Fikri Aji, Derry Wijaya
arXiv:2607. 19101v1 Announce Type: cross Abstract: Reliable Text Difficulty Assessment is a prerequisite for valid text simplification workflows and personalized learning applications.
By Yiheng Wu, Jue Hou, Roman Yangarber
The paper presents a multi‑label sentiment analysis framework that follows Plutchik’s wheel of emotions and is applicable to 100 languages. It introduces a dynamic weighting scheme to balance class contributions during training, adapts focal loss for harder instances, and derives a linear‑time method for selecting optimal class‑specific thresholds to maximize macro‑F1. Experiments show state‑of‑the‑art performance across multiple languages and metrics, and the authors release the code publicly.
By Selim F. Yilmaz, E. Batuhan Kaynak, Aykut Ko\c{c}, Hamdi Dibeklio\u{g}lu, Suleyman S. Kozat
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