SOTAVerified

Subjectivity Analysis

A related task to sentiment analysis is the subjectivity analysis with the goal of labeling an opinion as either subjective or objective.

Papers

Showing 125 of 63 papers

TitleStatusHype
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification TasksCode2
CAPO: Cost-Aware Prompt OptimizationCode2
Dual Contrastive Learning: Text Classification via Label-Aware Data AugmentationCode1
Entailment as Few-Shot LearnerCode1
Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task ApproachCode1
MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion VideosCode1
Universal Sentence EncoderCode1
Annotating Targets of Opinions in Arabic using Crowdsourcing0
A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles0
Good, Great, Excellent: Global Inference of Semantic Intensities0
Building and Modelling Multilingual Subjective Corpora0
BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches0
bwbaugh : Hierarchical sentiment analysis with partial self-training0
Experiments on Hybrid Corpus-Based Sentiment Lexicon Acquisition0
AMI\&ERIC: How to Learn with Naive Bayes and Prior Knowledge: an Application to Sentiment Analysis0
Exploring the Effects of Word Roots for Arabic Sentiment Analysis0
Connotation in Translation0
Concreteness and Subjectivity as Dimensions of Lexical Meaning0
Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis0
Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi0
Building a fine-grained subjectivity lexicon from a web corpus0
Discourse Connectors for Latent Subjectivity in Sentiment Analysis0
Complex and Precise Movie and Book Annotations in French Language for Aspect Based Sentiment Analysis0
Collocation Polarity Disambiguation Using Web-based Pseudo Contexts0
A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RoBERTa+DualCLAccuracy97.34Unverified
2BERT-Base + CLR + LSTMAccuracy97.3Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy97.1Unverified
4BERT-Base + LSTMAccuracy96.6Unverified
5AdaSentAccuracy95.5Unverified
6VLAWEAccuracy95Unverified
7CNN+MCFAAccuracy94.8Unverified
8byte mLSTM7Accuracy94.7Unverified
9Byte mLSTMAccuracy94.6Unverified
10USEAccuracy93.9Unverified
#ModelMetricClaimedVerifiedStatus
1XLM-R-LargeAccuracy93.56Unverified
2RobeCzechAccuracy93.29Unverified
3Czert-BAccuracy92.85Unverified
4Czech ElectraAccuracy91.85Unverified
5mBERTAccuracy91.23Unverified