← Back to all news
arXiv Computation and Language By Qinghua Zhao, Shuai Ma, Shuo Ren

KESA: A Knowledge Enhanced Approach For Sentiment Analysis

Read the original on arXiv Computation and Language →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv Machine Learning
Sep 11

Robust Multimodal Sentiment Analysis with Incomplete Modalities via Semantic-aware Completeness based Reconstruction

The paper presents a method for robust multimodal sentiment analysis that handles incomplete or noisy modalities. It introduces a completeness estimation technique to measure how much sentiment-relevant information remains in partial data, guiding the reconstruction of missing semantics. A joint training strategy stabilizes multi-task learning for sentiment prediction and completeness estimation, and experiments on three benchmark datasets show improved semantic reconstruction and sentiment accuracy.

By Han-Jun Choi, Byunggill Joe, Saim Shin, Jin Yea Jang
nlpmultimodalbenchmarks
More like this →
arXiv Machine Learning
4d ago

Enhancing Sentiment Classification on Small Datasets through Data Augmentation and Transfer Learning

The paper investigates how data augmentation can improve sentiment classification on small datasets. Three augmentation techniques—Easy Data Augmentation (EDA), back‑translation, and contextual token substitution—are evaluated with both traditional classifiers (Logistic Regression, Random Forest) and BERT. Results show that all methods boost performance, with contextual augmentation best for BERT and EDA/back‑translation benefiting traditional models.

By Mahmoud S. Mayaleh, Samer A. Mayaleh
llmsfine-tuning
More like this →
arXiv AI
Aug 26

SENSESHIFT: Continuous Sentiment-Controlled Text Generation via Encoder-based Mask Infilling

SenseShift is an encoder-based framework that enables fine‑grained, sentence‑level sentiment control in text generation. It uses bidirectional attention, quantized sentiment signals, and iterative mask infilling to generate local sentences conditioned on target sentiment intensity. Experiments on story and review generation show that SenseShift delivers stronger sentiment controllability while preserving text quality and robustness to out‑of‑domain inputs compared to larger decoder‑based baselines.

By Shahed Masoudian, Markus Frohmann, Emmanouil Karystinaios, Navid Rekabsaz, Markus Schedl
llmsefficiency
More like this →