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 5251–5300 of 5630 papers

TitleStatusHype
Deep Learning Brasil - NLP at SemEval-2020 Task 9: Sentiment Analysis of Code-Mixed Tweets Using Ensemble of Language Models—0
Deep learning for affective computing: text-based emotion recognition in decision support—0
Deep Learning for Climate Action: Computer Vision Analysis of Visual Narratives on X—0
Deep Learning for Digital Text Analytics: Sentiment Analysis—0
Deep Learning for NLP (without Magic)—0
Deep Learning for Sentiment Analysis - Invited Talk—0
Deep Learning Models for Sentiment Analysis in Arabic—0
Deep Learning Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis—0
Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets—0
Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings—0
Deep Learning Training Procedure Augmentations—0
Deep Learning versus Traditional Classifiers on Vietnamese Students' Feedback Corpus—0
Deep Markov Neural Network for Sequential Data Classification—0
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning—0
DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis—0
Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction—0
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language—0
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching—0
Deep neural network-based classification model for Sentiment Analysis—0
Deep Neural Networks with Massive Learned Knowledge—0
DeepNL: a Deep Learning NLP pipeline—0
Deep Recursive Neural Networks for Compositionality in Language—0
deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets—0
deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter—0
Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications—0
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach—0
Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models—0
Deep Sequence Models for Text Classification Tasks—0
Defending Multimodal Fusion Models against Single-Source Adversaries—0
DEFT2018 : recherche d'information et analyse de sentiments dans des tweets concernant les transports en \^Ile de France (DEFT2018 : Information Retrieval and Sentiment Analysis in Tweets about Public Transportation in \^Ile de France Region )—0
DeFTX: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer—0
Delivering Cognitive Behavioral Therapy Using A Conversational SocialRobot—0
De-Mixing Sentiment from Code-Mixed Text—0
Depeche Mood: a Lexicon for Emotion Analysis from Crowd Annotated News—0
DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News—0
Dependency Based Embeddings for Sentence Classification Tasks—0
Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification—0
Dependency Structure Augmented Contextual Scoping Framework for Multimodal Aspect-Based Sentiment Analysis—0
Depression detection from Social Media Bangla Text Using Recurrent Neural Networks—0
DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction—0
Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case—0
Description Based Text Classification with Reinforcement Learning—0
Design and Evaluation of SentiEcon: a fine-grained Economic/Financial Sentiment Lexicon from a Corpus of Business News—0
Design a Sustainable Micro-mobility Future: Trends and Challenges in the United States and European Union Using Natural Language Processing Techniques—0
Designing Heterogeneous LLM Agents for Financial Sentiment Analysis—0
Des repr\'esentations continues de mots pour l'analyse d'opinions en arabe: une \'etude qualitative (Word embeddings for Arabic sentiment analysis : a qualitative study)—0
Detecting and Resolving Shell Nouns in German—0
Detecting Conspiracy Theory Against COVID-19 Vaccines—0
Detecting Domain Dedicated Polar Words—0
Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon—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