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 1551–1600 of 5630 papers

TitleStatusHype
Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccination—0
BlogSet-BR: A Brazilian Portuguese Blog Corpus—0
Blinov: Distributed Representations of Words for Aspect-Based Sentiment Analysis at SemEval 2014—0
An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model—0
Blind signal decomposition of various word embeddings based on join and individual variance explained—0
Blending Ensemble for Classification with Genetic-algorithm generated Alpha factors and Sentiments (GAS)—0
``Haters gonna hate'': challenges for sentiment analysis of Facebook comments in Brazilian Portuguese—0
Bi-Transferring Deep Neural Networks for Domain Adaptation—0
Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference—0
Bitcoin's Edge: Embedded Sentiment in Blockchain Transactional Data—0
A Novel Way of Identifying Cyber Predators—0
Agreement and Disagreement: Comparison of Points of View in the Political Domain—0
Active Learning Over Multiple Domains in Natural Language Tasks—0
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification—0
Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments—0
A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency—0
BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment of Press Releases and Financial Text Around Inflection Points of Biotech Stocks—0
Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble—0
A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media—0
A Graphical User Interface for Feature-Based Opinion Mining—0
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification—0
Bilingual Word Representations with Monolingual Quality in Mind—0
Bilingual Sentiment Consistency for Statistical Machine Translation—0
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News—0
AgoraSpeech: A multi-annotated comprehensive dataset of political discourse through the lens of humans and AI—0
Active Learning for Imbalanced Sentiment Classification—0
Accountable Error Characterization—0
OneLove beyond the field -- A few-shot pipeline for topic and sentiment analysis during the FIFA World Cup in Qatar—0
Bilingual analysis of LOVE and HATRED emotional markers (SPSS-based approach)—0
Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications—0
BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis—0
A Novel Deep Learning Method for Textual Sentiment Analysis—0
A Gold Standard Dependency Corpus for English—0
Bi-directional Inter-dependencies of Subjective Expressions and Targets and their Value for a Joint Model—0
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis—0
Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey—0
A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis—0
Active learning for detection of stance components—0
BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning—0
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC—0
Bias in Emotion Recognition with ChatGPT—0
Eradicating Social Biases in Sentiment Analysis using Semantic Blinding and Semantic Propagation Graph Neural Networks—0
A Novel BGCapsule Network for Text Classification—0
Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction—0
Bias Beyond English: Counterfactual Tests for Bias in Sentiment Analysis in Four Languages—0
A novel Bayesian estimation-based word embedding model for sentiment analysis—0
标签先验知识增强的方面类别情感分析方法研究(Aspect-Category based Sentiment Analysis Enhanced by Label Prior Knowledge)—0
Agent-Based Simulations of Online Political Discussions: A Case Study on Elections in Germany—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