Subjectivity Analysis
18 papers with code • 2 benchmarks • 2 datasets
A related task to sentiment analysis is the subjectivity analysis with the goal of labeling an opinion as either subjective or objective.
Benchmarks
These leaderboards are used to track progress in Subjectivity Analysis
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
SUBJ
Latest papers
Czech Dataset for Cross-lingual Subjectivity Classification
pauli31/linear-transformation-4-cs-sa
•
•
LREC 2022
Our prime motivation is to provide a reliable dataset that can be used with the existing English dataset as a benchmark to test the ability of pre-trained multilingual models to transfer knowledge between Czech and English and vice versa.
Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
hiyouga/dual-contrastive-learning
•
•
Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings.
Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach
For the regression task, VADEC, when trained with SenWave, achieves 7. 6% and 16. 5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively.
Entailment as Few-Shot Learner
Large pre-trained language models (LMs) have demonstrated remarkable ability as few-shot learners.
An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks
Ritika2001/Word-Embedding-Models-for-Subjectivity-Analysis
•
•
IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) 2021
We obtained state-of-the-art results on the subjectivity task by fine-tuning the BERT Language Model.
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation
The Vector of Locally-Aggregated Word Embeddings (VLAWE) representation of a document is then computed by accumulating the differences between each codeword vector and each word vector (from the document) associated to the respective codeword.
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
makcedward/nlpaug
•
•
IJCNLP 2019
We present EDA: easy data augmentation techniques for boosting performance on text classification tasks.
Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web
To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source.
Translations as Additional Contexts for Sentence Classification
rktamplayo/MCFA
•
•
We are the first to use translations as domain-free contexts for sentence classification.
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations.
