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 1651–1700 of 5630 papers

TitleStatusHype
Distilling BERT for low complexity network training—0
Automatic Labelling of Topic Models Learned from Twitter by Summarisation—0
Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh—0
An end-to-end Neural Network Framework for Text Clustering—0
Automatic Music Mood Classification of Hindi Songs—0
An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM—0
Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records—0
Distinguishing Common and Proper Nouns—0
Distinguishing Literal and Non-Literal Usage of German Particle Verbs—0
Distributed Deep Learning Using Volunteer Computing-Like Paradigm—0
Distributed Real-Time Sentiment Analysis for Big Data Social Streams—0
Distributed Representations for Unsupervised Semantic Role Labeling—0
Common Space Embedding of Primal-Dual Relation Semantic Spaces—0
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation—0
Automatic Sarcasm Detection: A Survey—0
Distribution of Emotional Reactions to News Articles in Twitter—0
Dive deeper: Deep Semantics for Sentiment Analysis—0
Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning—0
Commonsense Reasoning for Identifying and Understanding the Implicit Need of Help and Synthesizing Assistive Actions—0
Automatic Triage of Mental Health Forum Posts—0
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis—0
COMMIT-P1WP3: A Co-occurrence Based Approach to Aspect-Level Sentiment Analysis—0
COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines—0
DLRG@DravidianLangTech-EACL2021: Transformer based approachfor Offensive Language Identification on Code-Mixed Tamil—0
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction—0
DN at SemEval-2023 Task 12: Low-Resource Language Text Classification via Multilingual Pretrained Language Model Fine-tuning—0
DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis—0
DNN Multimodal Fusion Techniques for Predicting Video Sentiment—0
A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content—0
A Neural Network Model for Low-Resource Universal Dependency Parsing—0
Do Convolutional Networks need to be Deep for Text Classification ?—0
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes—0
COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory—0
A Vector Space Approach for Aspect Based Sentiment Analysis—0
A Vietnamese Dialog Act Corpus Based on ISO 24617-2 standard—0
Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension—0
Document-Level Sentiment Analysis of Urdu Text Using Deep Learning Techniques—0
Document-level Sentiment Inference with Social, Faction, and Discourse Context—0
Document-Level Supervision for Multi-Aspect Sentiment Analysis Without Fine-grained Labels—0
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification—0
Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision—0
Does Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Task—0
Does `well-being' translate on Twitter?—0
Does BERT look at sentiment lexicon?—0
Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification—0
As Long as You Name My Name Right: Social Circles and Social Sentiment in the Hollywood Hearings—0
Does History Matter? Using Narrative Context to Predict the Trajectory of Sentence Sentiment—0
AWATIF: A Multi-Genre Corpus for Modern Standard Arabic Subjectivity and Sentiment Analysis—0
An Evaluation of the Brazilian Portuguese LIWC Dictionary for Sentiment Analysis—0
Combining Supervised and Unsupervised Enembles for Knowledge Base Population—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