arXiv:2507. 14022v2 Announce Type: replace-cross Abstract: This study proposes the Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) framework for document-level sentiment analysis.
arXiv:2606. 23701v1 Announce Type: cross Abstract: Qualitative product feedback can reveal nuanced user experiences, but its implicit sentiment is difficult to measure.
arXiv:2608. 04200v1 Announce Type: cross Abstract: Financial sentiment classifiers are commonly evaluated against human labels, but strong linguistic performance does not necessarily imply economically useful return predictability.
arXiv:2608. 09834v1 Announce Type: cross Abstract: Financial sentiment analysis converts unstructured financial news into quantitative signals that can support market analysis and decision-making.
This scoping review examines 421 studies (2015‑2026) on natural language processing applied to student evaluation of teaching comments. It maps the technical evolution from lexicons and classifiers to transformers and large language models, and evaluates four value dimensions. The review identifies a significant gap between actionable outputs (61.3%) and intended‑user evaluation (11.6%), highlighting limited progress in educational value and robustness.
arXiv:2601. 05261v2 Announce Type: replace-cross Abstract: Online consumer reviews are important decision-support resources in e-commerce, yet the increasing volume of reviews often creates information overload and makes it difficult for users to identify content that matches their individual preferences.
arXiv:2606. 04387v1 Announce Type: cross Abstract: Sales lead conversion in high-stakes domains (e.
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...
The paper compares Complement Naive Bayes (NB) with zero‑shot and few‑shot large language models (LLMs) across a wide range of model sizes and text classification tasks. NB outperforms LLMs when labeled data is available, achieving comparable accuracy to large LLMs while running thousands of samples per second on a CPU. In zero‑data sentiment settings, LLMs still dominate, but NB remains the best choice for resource‑constrained HPC practitioners, and the authors provide a Kubernetes Helm operator to automate model selection.
arXiv:2609.39049v1 Announce Type: cross Abstract: A large language model (LLM) can rate depression severity directly from a social media post or mark which clinical criteria the post shows and let co...
The paper presents an explainable hate‑speech detection framework that combines DistilBERT embeddings, a Bi‑LSTM network, and an attention mechanism to capture contextual and sequential information. It uses LIME to highlight influential text features, providing transparency in predictions. Evaluated on two benchmark datasets for both binary and multi‑class tasks, the model achieves F1‑scores of 96.78%–99.53% for binary classification and 94.99%–97.00% for multi‑class classification, outperforming existing baselines.
arXiv:2608. 07208v1 Announce Type: cross Abstract: Existing measures of how much a text is about a concept read the surface of the text: dictionary word shares, topic proportions, embedding similarities.
The authors present the Cross-Platform Fairness Evaluation (CPFE) framework, a five‑axis audit protocol that assesses discriminative performance, calibration, statistical significance, prediction equity, and attribution stability of transformer models. Applying CPFE to four models trained on a Kaggle mental‑health corpus and tested on Reddit and Twitter, they find substantial cross‑platform degradation in AUC (30–40%) and severe calibration failures (ECE rising to 0.5 on Twitter). The study demonstrates that platform‑specific temperature scaling can largely fix calibration without harming discrimination, while prediction equity and attribution stability analyses reveal significant disparities and vocabulary divergence across platforms. The results argue that cross‑platform validation across all CPFE axes should become a standard requirement for mental‑health NLP systems deployed in heterogeneous environments.