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 3701–3750 of 5630 papers

TitleStatusHype
Learning the Market: Sentiment-Based Ensemble Trading Agents—0
Learning to Adapt Credible Knowledge in Cross-lingual Sentiment Analysis—0
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis—0
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations—0
Learning to Detect Malicious Clients for Robust Federated Learning—0
Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis—0
Learning to Distill: The Essence Vector Modeling Framework—0
Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models—0
Learning to Generate Product Reviews from Attributes—0
Learning to Identify Subjective Sentences—0
Learning to Jointly Predict Ellipsis and Comparison Structures—0
Learning to Predict Distributions of Words Across Domains—0
Learning to Share by Masking the Non-shared for Multi-domain Sentiment Classification—0
Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis—0
Learning Using 1-Local Membership Queries—0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision—0
Learning when to skim and when to read—0
Learning with Noisy Labels for Sentence-level Sentiment Classification—0
Learning with Pseudo-Ensembles—0
Learning with Structured Representations for Negation Scope Extraction—0
Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets—0
Learning Word Embeddings for Low-Resource Languages by PU Learning—0
Learning Word Representations for Tunisian Sentiment Analysis—0
Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces—0
Learning Word Representations with Hierarchical Sparse Coding—0
Learning Word Representations with Regularization from Prior Knowledge—0
LEBANONUPRISING: a thorough study of Lebanese tweets—0
Legal Document Classification: An Application to Law Area Prediction of Petitions to Public Prosecution Service—0
Legal Sentiment Analysis and Opinion Mining (LSAOM): Assimilating Advances in Autonomous AI Legal Reasoning—0
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis—0
Less is More: Attention Supervision with Counterfactuals for Text Classification—0
Leveraging AI and Sentiment Analysis for Forecasting Election Outcomes in Mauritius—0
Leveraging Annotators' Gaze Behaviour for Coreference Resolution—0
Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification—0
Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis—0
Leveraging Cognitive Features for Sentiment Analysis—0
Leveraging Explainable AI to Analyze Researchers' Aspect-Based Sentiment about ChatGPT—0
Leveraging Fundamental Analysis for Stock Trend Prediction for Profit—0
Leveraging Language Identification to Enhance Code-Mixed Text Classification—0
Leveraging Linguistic Resources for Improving Neural Text Classification—0
Leveraging Multi-grained Sentiment Lexicon Information for Neural Sequence Models—0
Leveraging Multilingual Resources for Language Invariant Sentiment Analysis—0
Leveraging Multiple Domains for Sentiment Classification—0
Leveraging News Sentiment to Improve Microblog Sentiment Classification in the Financial Domain—0
Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic—0
Leveraging Pre-trained Language Model for Speech Sentiment Analysis—0
Leveraging Recursive Processing for Neural-Symbolic Affect-Target Associations—0
Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification—0
Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks—0
Leveraging Sparse and Dense Feature Combinations for Sentiment Classification—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