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 11511200 of 5630 papers

TitleStatusHype
Chinese Fine-Grained Financial Sentiment Analysis with Large Language ModelsCode0
AlbMoRe: A Corpus of Movie Reviews for Sentiment Analysis in AlbanianCode0
Document Embedding with Paragraph VectorsCode0
Does Transliteration Help Multilingual Language Modeling?Code0
Distributionally Robust Classifiers in Sentiment AnalysisCode0
Distributed Representations of Sentences and DocumentsCode0
Diverse Few-Shot Text Classification with Multiple MetricsCode0
An Operator Theoretic Approach for Analyzing Sequence Neural NetworksCode0
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion ModelingCode0
Distinguishing affixoid formations from compoundsCode0
ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility PredictionCode0
ClusterDataSplit: Exploring Challenging Clustering-Based Data Splits for Model Performance EvaluationCode0
Divide (Text) and Conquer (Sentiment): Improved Sentiment Classification by Constituent Conflict ResolutionCode0
Clustering in pure-attention hardmax transformers and its role in sentiment analysisCode0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data SetCode0
Distilling neural networks into skipgram-level decision listsCode0
Distilling Fine-grained Sentiment Understanding from Large Language ModelsCode0
CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media TextCode0
Distilling Task-Specific Knowledge from BERT into Simple Neural NetworksCode0
Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie ReviewsCode0
A Sentiment Analysis Dataset for Code-Mixed Malayalam-EnglishCode0
Distilling the Knowledge of Romanian BERTs Using Multiple TeachersCode0
CNN for Text-Based Multiple Choice Question AnsweringCode0
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment PredictionCode0
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasksCode0
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word EmbeddingCode0
Disambiguation of Verbal ShiftersCode0
Code-mixed Sentiment and Hate-speech PredictionCode0
Code-Switched Urdu ASR for Noisy Telephonic Environment using Data Centric Approach with Hybrid HMM and CNN-TDNNCode0
Characterizing Linguistic Shifts in Croatian News via Diachronic Word EmbeddingsCode0
Characterizing and Measuring Linguistic Dataset DriftCode0
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing SignalsCode0
A review of Spanish corpora annotated with negationCode0
Discovering Highly Influential Shortcut Reasoning: An Automated Template-Free ApproachCode0
DIBERT: Dependency Injected Bidirectional Encoder Representations from TransformersCode0
Differential Privacy Has Disparate Impact on Model AccuracyCode0
Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density EstimationCode0
A Clustering Analysis of Tweet Length and its Relation to SentimentCode0
Detection of Word Adversarial Examples in NLP: Benchmark and Baseline via Robust Density EstimationCode0
Explain Thyself Bully: Sentiment Aided Cyberbullying Detection with ExplanationCode0
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal InferenceCode0
Discrete Opinion Tree Induction for Aspect-based Sentiment AnalysisCode0
Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment ClassificationCode0
Combating Adversarial Misspellings with Robust Word RecognitionCode0
Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short TextsCode0
A Simple and Effective Approach for Fine Tuning Pre-trained Word Embeddings for Improved Text ClassificationCode0
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and DocumentsCode0
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful TechniquesCode0
A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text ClassificationCode0
Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word EmbeddingsCode0
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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