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 34013450 of 5630 papers

TitleStatusHype
Stronger Data Poisoning Attacks Break Data Sanitization DefensesCode1
Cyclegen: Cyclic consistency based product review generator from attributes0
Importance of Self-Attention for Sentiment Analysis0
Distantly Supervised Attribute Detection from Reviews0
When does deep multi-task learning work for loosely related document classification tasks?0
SyntaViz: Visualizing Voice Queries through a Syntax-Driven Hierarchical OntologyCode0
DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction0
Helping each Other: A Framework for Customer-to-Customer Suggestion Mining using a Semi-supervised Deep Neural Network0
Recurrent Attention Unit0
Topic-Specific Sentiment Analysis Can Help Identify Political Ideology0
Variational Semi-supervised Aspect-term Sentiment Analysis via Transformer0
Learning Robust Joint Representations for Multimodal Sentiment Analysis0
On Zero-shot Cross-lingual Transfer of Multilingual Neural Machine Translation0
Compositional Coding Capsule Network with K-Means Routing for Text ClassificationCode0
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach0
Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New ExperimentsCode0
Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform0
Super Characters: A Conversion from Sentiment Classification to Image Classification0
Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning0
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingCode3
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful TechniquesCode0
Text-based Sentiment Analysis and Music Emotion Recognition0
Training Complex Models with Multi-Task Weak SupervisionCode0
Inhibited Softmax for Uncertainty Estimation in Neural NetworksCode0
結合卷積神經網路與遞迴神經網路於推文極性分類 (Combining Convolutional Neural Network and Recurrent Neural Network for Tweet Polarity Classification) [In Chinese]0
An LSTM Approach to Short Text Sentiment Classification with Word Embeddings0
DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep LearningCode0
Sentiment analysis under temporal shift0
Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation0
Sentiment Expression Boundaries in Sentiment Polarity Classification0
Linking News Sentiment to Microblogs: A Distributional Semantics Approach to Enhance Microblog Sentiment Classification0
Learning representations for sentiment classification using Multi-task framework0
SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification0
Saying no but meaning yes: negation and sentiment analysis in Basque0
Disney at IEST 2018: Predicting Emotions using an Ensemble0
Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings0
BrainT at IEST 2018: Fine-tuning Multiclass Perceptron For Implicit Emotion ClassificationCode0
Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings0
UWB at IEST 2018: Emotion Prediction in Tweets with Bidirectional Long Short-Term Memory Neural Network0
The Role of Emotions in Native Language Identification0
Thumbs Up and Down: Sentiment Analysis of Medical Online Forums0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition0
USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection0
Aspect Based Sentiment Analysis into the Wild0
Arabizi sentiment analysis based on transliteration and automatic corpus annotation0
NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification0
NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level PreprocessingCode0
Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions0
Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data0
Sequence Classification with Human AttentionCode0
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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