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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4901–4950 of 5630 papers

TitleStatusHype
Building Chinese Affective Resources in Valence-Arousal Dimensions—0
Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach—0
Building Sentiment Lexicons for All Major Languages—0
Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings—0
Building Web-Interfaces for Vector Semantic Models with the WebVectors Toolkit—0
bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis—0
BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches—0
BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection—0
bwbaugh : Hierarchical sentiment analysis with partial self-training—0
C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering—0
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
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification—0
CalBERT - Code-mixed Adaptive Language representations using BERT—0
Calling to CNN-LSTM for Rumor Detection: A Deep Multi-channel Model for Message Veracity Classification in Microblogs—0
Can a Humanoid Robot be part of the Organizational Workforce? A User Study Leveraging Sentiment Analysis—0
Can AI Read Between The Lines? Benchmarking LLMs On Financial Nuance—0
Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models—0
Can tweets predict article retractions? A comparison between human and LLM labelling—0
Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks—0
CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis—0
Can Data Diversity Enhance Learning Generalization?—0
Can Domain Adaptation be Handled as Analogies?—0
CAN\'EPHORE : un corpus fran pour la fouille d'opinion cibl\'ee—0
Can I Hear You? Sentiment Analysis on Medical Forums—0
Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations—0
Can Modern Standard Arabic Approaches be used for Arabic Dialects? Sentiment Analysis as a Case Study—0
Can Pre-trained Language Models Interpret Similes as Smart as Human?—0
Can Sentiment Analysis Reveal Structure in a Plotless Novel?—0
Can the Crowd be Controlled?: A Case Study on Crowd Sourcing and Automatic Validation of Completed Tasks based on User Modeling—0
Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer—0
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification—0
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling—0
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best--Worst Scaling—0
Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network—0
CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean—0
Cardiovascular Disease Risk Prediction via Social Media—0
CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment—0
Cascading Multiway Attentions for Document-level Sentiment Classification—0
CAT: Credibility Analysis of Arabic Content on Twitter—0
Categorical Probability Proportion Difference (CPPD): A Feature Selection Method for Sentiment Classification—0
Category Enhanced Word Embedding—0
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation—0
CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms—0
Causal Interpretation of Self-Attention in Pre-Trained Transformers—0
Causal Intervention Improves Implicit Sentiment Analysis—0
Causal Investigation of Public Opinion during the COVID-19 Pandemic via Social Media Text—0
Causality Analysis of Twitter Sentiments and Stock Market Returns—0
Causality between Sentiment and Cryptocurrency Prices—0
Causing Emotion in Collocation:An Exploratory Data Analysis—0
Cautious Monotonicity in Case-Based Reasoning with Abstract Argumentation—0
Show:102550
← PrevPage 99 of 113Next →
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