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 1801–1850 of 5630 papers

TitleStatusHype
Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models—0
TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect—0
Metamorphic Adversarial Detection Pipeline for Face Recognition Systems—0
Visual Sentiment Analysis: A Natural DisasterUse-case Task at MediaEval 2021—0
Isomer: Transfer enhanced Dual-Channel Heterogeneous Dependency Attention Network for Aspect-based Sentiment Classification—0
Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism—0
AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation—0
An Improved Reinforcement Learning Model Based on Sentiment Analysis—0
Lexicon-based Methods vs. BERT for Text Sentiment Analysis—0
Does BERT look at sentiment lexicon?—0
SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural SpeechCode1
Emojional: Emoji EmbeddingsCode1
Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text—0
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis ModelCode0
How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis TaskCode0
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps—0
PoliSe: Reinforcing Politeness using User Sentiment for Customer Care Response Generation—0
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis—0
KESA: A Knowledge Enhanced Approach For Sentiment Analysis—0
Prompt-Learning for Fine-Grained Entity Typing—0
Can Pre-trained Language Models Interpret Similes as Smart as Human?—0
A Probabilistic Framework for Analyzing Moral Perspectives in the COVID-19 Vaccine Debate—0
Question Answering Infused Pre-training of General-Purpose Contextualized Representations—0
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis—0
BACN: Bi-direction Attention Capsule-based Network for Multimodal Sentiment Analysis—0
Sentence-level Privacy for Document Embeddings—0
Discriminative Models Still Outperform Generative Models in Aspect Based Sentiment Analysis In Cross-Domain and Cross-Lingual Settings—0
Direct parsing to sentiment graphs—0
FeelsGoodMan: Inferring Semantics of Twitch Neologisms—0
Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis—0
Pre-training Pre-trained Models with Auxiliary Labels and Fine-tuning for Text Classification—0
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis—0
Graph-based Fine-grained Multimodal Attention Mechanism for Sentiment Analysis—0
CluSent – Combining Semantic Expansion and De-Noising for Dataset-Oriented Sentiment Analysis of Short Texts—0
Multimodal Sentiment Analysis with Common-sense Modulation—0
Challenges for Open-domain Targeted Sentiment Analysis—0
CalBERT - Code-mixed Adaptive Language representations using BERT—0
Detection of Adversarial Examples in NLP: Benchmark and Baseline via Robust Density EstimationCode0
A System to Filter out Unwanted Social Media Content in Real-time on iPhones—0
A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis—0
SAMBERT: Improve Aspect Sentiment Triplet Extraction by Segmenting the Attention Maps of BERT—0
IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languagesCode0
Sentiment Analysis of Fashion Related Posts in Social Media—0
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning ApproachCode1
Automatic evaluation of scientific abstracts through natural language processing—0
Forecasting Crude Oil Price Using Event Extraction—0
Contrastive Clustering: Toward Unsupervised Bias Reduction for Emotion and Sentiment Classification—0
Improving usual Naive Bayes classifier performances with Neural Naive Bayes based models—0
Learning Data Teaching Strategies Via Knowledge Tracing—0
Dataset of Philippine Presidents Speeches from 1935 to 2016—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