SOTAVerified

Sentiment Analysis

Sentiment Analysis is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment.

Sentiment Analysis techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.

More recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.

Further readings:

Papers

Showing 26512700 of 5630 papers

TitleStatusHype
Leveraging Multilingual Resources for Language Invariant Sentiment Analysis0
Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification0
Structured Self-AttentionWeights Encode Semantics in Sentiment AnalysisCode0
Cross-lingual sentiment classification in low-resource Bengali languageCode0
A structure-enhanced graph convolutional network for sentiment analysis0
Noisy Text Data: Achilles’ Heel of BERT0
Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis0
Transformer-based Multi-Aspect Modeling for Multi-Aspect Multi-Sentiment Analysis0
On the Reliability and Validity of Detecting Approval of Political Actors in Tweets0
A Shared-Private Representation Model with Coarse-to-Fine Extraction for Target Sentiment Analysis0
ClusterDataSplit: Exploring Challenging Clustering-Based Data Splits for Model Performance EvaluationCode0
ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset0
Less is More: Attention Supervision with Counterfactuals for Text Classification0
Inducing Target-Specific Latent Structures for Aspect Sentiment Classification0
Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis0
Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting0
Global Sentiment Analysis Of COVID-19 Tweets Over TimeCode0
Interpretation of NLP models through input marginalization0
Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning0
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation0
How angry are your customers? Sentiment analysis of support tickets that escalate0
Transgender Community Sentiment Analysis from Social Media Data: A Natural Language Processing Approach0
Pretraining and Fine-Tuning Strategies for Sentiment Analysis of Latvian TweetsCode0
A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User ReviewsCode0
Multiple-element joint detection for Aspect-Based Sentiment Analysis0
Quasi Error-free Text Classification and Authorship Recognition in a large Corpus of English Literature based on a Novel Feature Set0
LT3 at SemEval-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of Hinglish Social Media Text0
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search0
JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language TagsCode0
Teacher-Student Consistency For Multi-Source Domain AdaptationCode0
Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums0
Multi-task Learning of Negation and Speculation for Targeted Sentiment ClassificationCode0
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus DetectionCode0
NUIG-Shubhanker@Dravidian-CodeMix-FIRE2020: Sentiment Analysis of Code-Mixed Dravidian text using XLNet0
Learning Word Representations for Tunisian Sentiment Analysis0
Temperature check: theory and practice for training models with softmax-cross-entropy losses0
CrowdCog: A Cognitive Skill based System for Heterogeneous Task Assignment and Recommendation in CrowdsourcingCode0
Controlling the Interaction Between Generation and Inference in Semi-Supervised Variational Autoencoders Using Importance WeightingCode0
Legal Document Classification: An Application to Law Area Prediction of Petitions to Public Prosecution Service0
Structured Self-Attention Weights Encode Semantics in Sentiment AnalysisCode0
HPCC-YNU at SemEval-2020 Task 9: A Bilingual Vector Gating Mechanism for Sentiment Analysis of Code-Mixed TextCode0
gundapusunil at SemEval-2020 Task 9: Syntactic Semantic LSTM Architecture for SENTIment Analysis of Code-MIXed Data0
gundapusunil at SemEval-2020 Task 8: Multimodal Memotion Analysis0
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation0
A Multi-Task Incremental Learning Framework with Category Name Embedding for Aspect-Category Sentiment AnalysisCode0
Sentiment Analysis for Roman Urdu Text over Social Media, a Comparative Study0
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation0
Sentiment Analysis for Reinforcement Learning0
Aspect-Based Sentiment Analysis in Education Domain0
TextDecepter: Hard Label Black Box Attack on Text Classification0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100Unverified
2MT-DNN-SMARTAccuracy97.5Unverified
3T5-11BAccuracy97.5Unverified
4MUPPET Roberta LargeAccuracy97.4Unverified
5T5-3BAccuracy97.4Unverified
6ALBERTAccuracy97.1Unverified
7StructBERTRoBERTa ensembleAccuracy97.1Unverified
8XLNet (single model)Accuracy97Unverified
9SMARTRoBERTaDev Accuracy96.9Unverified
10ELECTRAAccuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large with LlamBERTAccuracy96.68Unverified
2RoBERTa-largeAccuracy96.54Unverified
3XLNetAccuracy96.21Unverified
4Heinsen Routing + RoBERTa LargeAccuracy96.2Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.1Unverified
6GraphStarAccuracy96Unverified
7DV-ngrams-cosine with NB sub-sampling + RoBERTa.baseAccuracy95.94Unverified
8DV-ngrams-cosine + RoBERTa.baseAccuracy95.92Unverified
9Roberta_Large ST + Cosine Similarity LossAccuracy95.9Unverified
10BERT large finetune UDAAccuracy95.8Unverified
#ModelMetricClaimedVerifiedStatus
1Llama-3.3-70B + CAPOAccuracy62.27Unverified
2Mistral-Small-24B + CAPOAccuracy 60.2Unverified
3Heinsen Routing + RoBERTa LargeAccuracy59.8Unverified
4RoBERTa-large+Self-ExplainingAccuracy59.1Unverified
5Qwen2.5-32B + CAPOAccuracy 59.07Unverified
6Heinsen Routing + GPT-2Accuracy58.5Unverified
7BCN+Suffix BiLSTM-Tied+CoVeAccuracy56.2Unverified
8BERT LargeAccuracy55.5Unverified
9LM-CPPF RoBERTa-baseAccuracy54.9Unverified
10BCN+ELMoAccuracy54.7Unverified
#ModelMetricClaimedVerifiedStatus
1Char-level CNNError4.88Unverified
2SVDCNNError4.74Unverified
3LEAMError4.69Unverified
4fastText, h=10, bigramError4.3Unverified
5SWEM-hierError4.19Unverified
6SRNNError3.96Unverified
7M-ACNNError3.89Unverified
8DNC+CUWError3.6Unverified
9CCCapsNetError3.52Unverified
10Block-sparse LSTMError3.27Unverified
#ModelMetricClaimedVerifiedStatus
1Millions of EmojiTraining Time1,500Unverified
2VLAWEAccuracy93.3Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy92.5Unverified
4AnglE-LLaMA-7BAccuracy91.09Unverified
5byte mLSTM7Accuracy86.8Unverified
6MEANAccuracy84.5Unverified
7RNN-CapsuleAccuracy83.8Unverified
8Capsule-BAccuracy82.3Unverified
9SuBiLSTM-TiedAccuracy81.6Unverified
10USE_T+CNNAccuracy81.59Unverified