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 5351–5400 of 5630 papers

TitleStatusHype
Discriminative Models Can Still Outperform Generative Models in Aspect Based Sentiment Analysis—0
Discriminative Models Still Outperform Generative Models in Aspect Based Sentiment Analysis In Cross-Domain and Cross-Lingual Settings—0
Discriminative Neural Sentence Modeling by Tree-Based Convolution—0
Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning—0
Disney at IEST 2018: Predicting Emotions using an Ensemble—0
Dissecting the Practical Lexical Function Model for Compositional Distributional Semantics—0
DisSim-FinBERT: Text Simplification for Core Message Extraction in Complex Financial Texts—0
Distance Based Source Domain Selection for Sentiment Classification—0
Distance Metric Learning for Aspect Phrase Grouping—0
Distantly Supervised Aspect Clustering And Naming For E-Commerce Reviews—0
Distantly Supervised Attribute Detection from Reviews—0
Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance—0
Distilling BERT for low complexity network training—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
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation—0
Distribution of Emotional Reactions to News Articles in Twitter—0
Dive deeper: Deep Semantics for Sentiment Analysis—0
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification—0
DLIREC: Aspect Term Extraction and Term Polarity Classification System—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
Do Convolutional Networks need to be Deep for Text Classification ?—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
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
On Commonsense Cues in BERT for Solving Commonsense Tasks—0
Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models—0
Does History Matter? Using Narrative Context to Predict the Trajectory of Sentence Sentiment—0
Does Size Matter? Text and Grammar Revision for Parsing Social Media Data—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations—0
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance—0
Do Large Language Models Possess Sensitive to Sentiment?—0
Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering—0
Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation—0
Domain Adaptation for Sentiment Analysis using Keywords in the Target Domain as the Learning Weight—0
Domain Adaptation of Polarity Lexicon combining Term Frequency and Bootstrapping—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