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 3651–3700 of 5630 papers

TitleStatusHype
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks—0
Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis—0
Learning a Max-Margin Classifier for Cross-Domain Sentiment Analysis—0
Learning Better Embeddings for Rare Words Using Distributional Representations—0
Learning Better Sentence Representation with Syntax Information—0
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision—0
Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification—0
Learning cascaded latent variable models for biomedical text classification—0
Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network—0
Learning Context-Sensitive Convolutional Filters for Text Processing—0
Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information—0
Learning Contextually Informed Representations for Linear-Time Discourse Parsing—0
Learning Cross-lingual Word Embeddings via Matrix Co-factorization—0
Learning Data Teaching Strategies Via Knowledge Tracing—0
Learning Distributed Representations for Multilingual Text Sequences—0
Learning Domain Representation for Multi-Domain Sentiment Classification—0
Learning Domain-Sensitive and Sentiment-Aware Word Embeddings—0
Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts—0
Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases—0
Learning Explicit and Implicit Structures for Targeted Sentiment Analysis—0
Learning for Microblogs with Distant Supervision: Political Forecasting with Twitter—0
Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning—0
Learning from Bullying Traces in Social Media—0
Learning from Domain Complexity—0
Learning from students' perception on professors through opinion mining—0
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration—0
Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis—0
Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach—0
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis—0
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets—0
Learning Latent Word Representations for Domain Adaptation using Supervised Word Clustering—0
Learning low dimensional word based linear classifiers using Data Shared Adaptive Bootstrap Aggregated Lasso with application to IMDb data—0
Learning Phrase Embeddings from Paraphrases with GRUs—0
Learning Relationship between Authors' Activity and Sentiments: A case study of online medical forums—0
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis—0
Learning representations for sentiment classification using Multi-task framework—0
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization—0
Learning Robust Joint Representations for Multimodal Sentiment Analysis—0
Learning Rules-First Classifiers—0
Learning Scalar Adjective Intensity from Paraphrases—0
Learning Semantic Representations of Users and Products for Document Level Sentiment Classification—0
Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification—0
Learning Sentence Representations over Tree Structures for Target-Dependent Classification—0
Learning Sentiment Composition from Sentiment Lexicons—0
Learning Sentiment Lexicons in Spanish—0
Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits—0
Learning Structures of Negations from Flat Annotations—0
Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network—0
Learning Term Embeddings for Taxonomic Relation Identification Using Dynamic Weighting Neural Network—0
Learning text representation using recurrent convolutional neural network with highway layers—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