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 36013650 of 5630 papers

TitleStatusHype
NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation0
CNN for Text-Based Multiple Choice Question AnsweringCode0
Twitter Universal Dependency Parsing for African-American and Mainstream American English0
Modeling Mistrust in End-of-Life CareCode0
Enhancing Sentence Embedding with Generalized PoolingCode0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis0
The Natural Language Decathlon: Multitask Learning as Question AnsweringCode1
Using J-K fold Cross Validation to Reduce Variance When Tuning NLP ModelsCode0
Aspect Sentiment Classification with both Word-level and Clause-level AttentionNetworks0
An Improved Text Sentiment Classification Model Using TF-IDF and Next Word Negation0
Multimodal Sentiment Analysis using Hierarchical Fusion with Context ModelingCode0
Aspect Sentiment Model for Micro ReviewsCode0
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable RepresentationsCode0
Cold-Start Aware User and Product Attention for Sentiment ClassificationCode1
Highly Relevant Routing Recommendation Systems for Handling Few Data Using MDL Principle and Embedded Relevance Boosting Factors0
Crowd-Powered Data Mining0
Bringing replication and reproduction together with generalisability in NLP: Three reproduction studies for Target Dependent Sentiment AnalysisCode0
Exploiting Document Knowledge for Aspect-level Sentiment ClassificationCode0
An Ensemble Model for Sentiment Analysis of Hindi-English Code-Mixed Data0
Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse DomainsCode0
Addition of Code Mixed Features to Enhance the Sentiment Prediction of Song Lyrics0
Cross-Lingual Task-Specific Representation Learning for Text Classification in Resource Poor Languages0
Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data0
Emoji-Powered Representation Learning for Cross-Lingual Sentiment ClassificationCode0
Multimodal Relational Tensor Network for Sentiment and Emotion Classification0
Semi-supervised and Transfer learning approaches for low resource sentiment classification0
Information Aggregation via Dynamic Routing for Sequence EncodingCode0
Psychological State in Text: A Limitation of Sentiment Analysis0
Learning Semantic Sentence Embeddings using Sequential Pair-wise DiscriminatorCode0
Sentiment Analysis on Social Network: Using Emoticon Characteristics for Twitter Polarity Classification0
Weakly Supervised Coupled Networks for Visual Sentiment AnalysisCode0
Detecting and Resolving Shell Nouns in German0
Social and Emotional Correlates of Capitalization on Twitter0
A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection0
Entropy-Based Subword Mining with an Application to Word Embeddings0
Neural Metaphor Detecting with CNN-LSTM Model0
CLUF: a Neural Model for Second Language Acquisition Modeling0
Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text ClassificationCode0
IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction0
NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks Through a Multi-Domain Sentiment Model0
THU\_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction0
Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction0
HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection0
Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition0
ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings0
SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection0
SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron0
INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter0
Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection0
LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony Detection0
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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