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 3051–3100 of 5630 papers

TitleStatusHype
SAIL: A hybrid approach to sentiment analysis—0
SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features—0
SALAD: Improving Robustness and Generalization through Contrastive Learning with Structure-Aware and LLM-Driven Augmented Data—0
Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification—0
SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media—0
SAMBERT: Improve Aspect Sentiment Triplet Extraction by Segmenting the Attention Maps of BERT—0
Sample-to-Sample Correspondence for Unsupervised Domain Adaptation—0
Experiences from Creating a Benchmark for Sentiment Classification for Varieties of English—0
SANA: A Large Scale Multi-Genre, Multi-Dialect Lexicon for Arabic Subjectivity and Sentiment Analysis—0
SANA : Sentiment Analysis on Newspapers comments in Algeria—0
SAP-RI: A Constrained and Supervised Approach for Aspect-Based Sentiment Analysis—0
SAP-RI: Twitter Sentiment Analysis in Two Days—0
Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation—0
Sarcasm as Contrast between a Positive Sentiment and Negative Situation—0
Sarcasm Detection: A Comparative Study—0
sarcasm detection and quantification in arabic tweets—0
Sarcasm Detection : Building a Contextual Hierarchy—0
Sarcasm Detection Framework Using Context, Emotion and Sentiment Features—0
Sarcasm Detection in Chinese Using a Crowdsourced Corpus—0
Sarcasm Detection on Czech and English Twitter—0
Sarcastic Soulmates: Intimacy and irony markers in social media messaging—0
SARF: Enhancing Stock Market Prediction with Sentiment-Augmented Random Forest—0
SA-UZH: Verb-based Sentiment Analysis—0
Saying no but meaning yes: negation and sentiment analysis in Basque—0
SB-CH: A Swiss German Corpus with Sentiment Annotations—0
SBTRec- A Transformer Framework for Personalized Tour Recommendation Problem with Sentiment Analysis—0
Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions—0
Scalable Multi-phase Word Embedding Using Conjunctive Propositional Clauses—0
Scalable Statistical Relational Learning for NLP—0
ScaleVLAD: Improving Multimodal Sentiment Analysis via Multi-Scale Fusion of Locally Descriptors—0
Scaling Federated Learning for Fine-tuning of Large Language Models—0
Scaling Semi-supervised Naive Bayes with Feature Marginals—0
SCARE ― The Sentiment Corpus of App Reviews with Fine-grained Annotations in German—0
ScenarioSA: A Large Scale Conversational Database for Interactive Sentiment Analysis—0
SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning—0
Scoping natural language processing in Indonesian and Malay for education applications—0
Scrutable Feature Sets for Stance Classification—0
SCUDS at ROCLING-2021 Shared Task: Using Pretrained Model for Dimensional Sentiment Analysis Based on Sample Expansion Method—0
Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings—0
Seek Common Ground While Reserving Differences: Semi-Supervised Image-Text Sentiment Recognition—0
Seeking Sinhala Sentiment: Predicting Facebook Reactions of Sinhala Posts—0
SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis—0
Sejarah dan Perkembangan Teknik Natural Language Processing (NLP) Bahasa Indonesia: Tinjauan tentang sejarah, perkembangan teknologi, dan aplikasi NLP dalam bahasa Indonesia—0
Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification—0
Self-Reflective Sentiment Analysis—0
*SEM 2012 Shared Task: Resolving the Scope and Focus of Negation—0
Semantic Consistency Regularization with Large Language Models for Semi-supervised Sentiment Analysis—0
Semantic descriptions of 24 evaluational adjectives, for application in sentiment analysis—0
Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification—0
Semantic Equivalence Detection: Are Interrogatives Harder than Declaratives?—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
2T5-11BAccuracy97.5—Unverified
3MT-DNN-SMARTAccuracy97.5—Unverified
4T5-3BAccuracy97.4—Unverified
5MUPPET Roberta LargeAccuracy97.4—Unverified
6StructBERTRoBERTa ensembleAccuracy97.1—Unverified
7ALBERTAccuracy97.1—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