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

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Showing 2501–2550 of 5630 papers

TitleStatusHype
A Stochastic Time Series Model for Predicting Financial Trends using NLP—0
Scaling Federated Learning for Fine-tuning of Large Language Models—0
ShufText: A Simple Black Box Approach to Evaluate the Fragility of Text Classification Models—0
If you've got it, flaunt it: Making the most of fine-grained sentiment annotations—0
BERTaú: Itaú BERT for digital customer service—0
Muppet: Massive Multi-task Representations with Pre-FinetuningCode0
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks—0
Transfer Learning Approach for Detecting Psychological Distress in Brexit Tweets—0
Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh—0
Are Top School Students More Critical of Their Professors? Mining Comments on RateMyProfessor.com—0
Arabic aspect based sentiment analysis using bidirectional GRU based models—0
CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media TextCode0
Comparison of Machine Learning for Sentiment Analysis in Detecting Anxiety Based on Social Media Data—0
The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements—0
Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis—0
Deep Learning applications for COVID-19—0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data SetCode0
Learning Better Sentence Representation with Syntax Information—0
Effect of Word Embedding Variable Parameters on Arabic Sentiment Analysis Performance—0
BRDS: An FPGA-based LSTM Accelerator with Row-Balanced Dual-Ratio Sparsification—0
Mining the Relationship Between COVID-19 Sentiment and Market Performance—0
A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis—0
Learning Neural Networks on SVD Boosted Latent Spaces for Semantic ClassificationCode0
Sentiment Analysis for Open Domain Conversational Agent—0
Assessing Emoji Use in Modern Text Processing ToolsCode0
Meta Auxiliary Labels with Constituent-based Transformer for Aspect-based Sentiment Analysis—0
Domain Adaptation via Anaomaly Detection—0
SEQUENCE-LEVEL FEATURES: HOW GRU AND LSTM CELLS CAPTURE N-GRAMS—0
WordsWorth Scores for Attacking CNNs and LSTMs for Text Classification—0
Learning a Max-Margin Classifier for Cross-Domain Sentiment Analysis—0
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks—0
Creating Domain Dependent Turkish WordNet and SentiNet—0
Aspect-based Sentiment Classification via Reinforcement Learning—0
Online Limited Memory Neural-Linear Bandits—0
HisNet: A Polarity Lexicon based on WordNet for Emotion Analysis—0
TextTN: Probabilistic Encoding of Language on Tensor Network—0
Feedforward Legendre Memory Unit—0
On the Robustness of Sentiment Analysis for Stock Price Forecasting—0
ERNIE-Doc: A Retrospective Long-Document Modeling TransformerCode0
CLEAR: Contrastive Learning for Sentence Representation—0
Out of Order: How Important Is The Sequential Order of Words in a Sentence in Natural Language Understanding Tasks?—0
A multi-task learning network using shared BERT models for aspect-based sentiment analysis—0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
Undivided Attention: Are Intermediate Layers Necessary for BERT?—0
Should I visit this place? Inclusion and Exclusion Phrase Mining from ReviewsCode0
Building domain specific lexicon based on TikTok comment datasetCode0
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration—0
Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution—0
MeisterMorxrc at SemEval-2020 Task 9: Fine-Tune Bert and Multitask Learning for Sentiment Analysis of Code-Mixed Tweets—0
"Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study—0
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All datasetsSST-2 Binary classificationIMDbSST-5 Fine-grained classificationYelp Binary classificationMRYelp Fine-grained classificationBanglaBookDynaSentSST-3Sentiment MergedUser and product informationAmazon Review Full

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100—Unverified
2MT-DNN-SMARTAccuracy97.5—Unverified
3T5-11BAccuracy97.5—Unverified
4MUPPET Roberta LargeAccuracy97.4—Unverified
5T5-3BAccuracy97.4—Unverified
6ALBERTAccuracy97.1—Unverified
7StructBERTRoBERTa ensembleAccuracy97.1—Unverified
8XLNet (single model)Accuracy97—Unverified
9SMARTRoBERTaDev Accuracy96.9—Unverified
10ELECTRAAccuracy96.9—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large with LlamBERTAccuracy96.68—Unverified
2RoBERTa-largeAccuracy96.54—Unverified
3XLNetAccuracy96.21—Unverified
4Heinsen Routing + RoBERTa LargeAccuracy96.2—Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.1—Unverified
6GraphStarAccuracy96—Unverified
7DV-ngrams-cosine with NB sub-sampling + RoBERTa.baseAccuracy95.94—Unverified
8DV-ngrams-cosine + RoBERTa.baseAccuracy95.92—Unverified
9Roberta_Large ST + Cosine Similarity LossAccuracy95.9—Unverified
10BERT large finetune UDAAccuracy95.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Llama-3.3-70B + CAPOAccuracy62.27—Unverified
2Mistral-Small-24B + CAPOAccuracy 60.2—Unverified
3Heinsen Routing + RoBERTa LargeAccuracy59.8—Unverified
4RoBERTa-large+Self-ExplainingAccuracy59.1—Unverified
5Qwen2.5-32B + CAPOAccuracy 59.07—Unverified
6Heinsen Routing + GPT-2Accuracy58.5—Unverified
7BCN+Suffix BiLSTM-Tied+CoVeAccuracy56.2—Unverified
8BERT LargeAccuracy55.5—Unverified
9LM-CPPF RoBERTa-baseAccuracy54.9—Unverified
10BCN+ELMoAccuracy54.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Char-level CNNError4.88—Unverified
2SVDCNNError4.74—Unverified
3LEAMError4.69—Unverified
4fastText, h=10, bigramError4.3—Unverified
5SWEM-hierError4.19—Unverified
6SRNNError3.96—Unverified
7M-ACNNError3.89—Unverified
8DNC+CUWError3.6—Unverified
9CCCapsNetError3.52—Unverified
10Block-sparse LSTMError3.27—Unverified
#ModelMetricClaimedVerifiedStatus
1Millions of EmojiTraining Time1,500—Unverified
2VLAWEAccuracy93.3—Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy92.5—Unverified
4AnglE-LLaMA-7BAccuracy91.09—Unverified
5byte mLSTM7Accuracy86.8—Unverified
6MEANAccuracy84.5—Unverified
7RNN-CapsuleAccuracy83.8—Unverified
8Capsule-BAccuracy82.3—Unverified
9SuBiLSTM-TiedAccuracy81.6—Unverified
10USE_T+CNNAccuracy81.59—Unverified