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 151–200 of 5630 papers

TitleStatusHype
Sentiment analysis of texts from social networks based on machine learning methods for monitoring public sentiment—0
FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and AnalysisCode1
An End-to-End Homomorphically Encrypted Neural Network—0
Coherency Improved Explainable Recommendation via Large Language Model—0
SentiFormer: Metadata Enhanced Transformer for Image Sentiment AnalysisCode0
Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments—0
Effects of Prompt Length on Domain-specific Tasks for Large Language Models—0
Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development—0
Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis—0
Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection—0
Task-Informed Anti-Curriculum by Masking Improves Downstream Performance on TextCode0
MSE-Adapter: A Lightweight Plugin Endowing LLMs with the Capability to Perform Multimodal Sentiment Analysis and Emotion RecognitionCode1
Performance Evaluation of Sentiment Analysis on Text and Emoji Data Using End-to-End, Transfer Learning, Distributed and Explainable AI Models—0
Understanding and Tackling Label Errors in Individual-Level Nature Language UnderstandingCode0
Subjective Logic EncodingsCode0
Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto ForecastingCode1
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment AnalysisCode1
Text Classification in the LLM Era - Where do we stand?—0
CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment—0
Exploring Emotion-Sensitive LLM-Based Conversational AI—0
SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text GenerationCode0
Using Contextually Aligned Online Reviews to Measure LLMs' Performance Disparities Across Language Varieties—0
How does a Multilingual LM Handle Multiple Languages?—0
LLaVAC: Fine-tuning LLaVA as a Multimodal Sentiment ClassifierCode0
Aligning Human and Machine Attention for Enhanced Supervised Learning—0
Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction—0
FinRLlama: A Solution to LLM-Engineered Signals Challenge at FinRL Contest 2024Code1
An End-To-End LLM Enhanced Trading System—0
Meursault as a Data Point—0
Explainable AI for Sentiment Analysis of Human Metapneumovirus (HMPV) Using XLNet—0
Benchmark on Peer Review Toxic Detection: A Challenging Task with a New Dataset—0
Large Language Models' Accuracy in Emulating Human Experts' Evaluation of Public Sentiments about Heated Tobacco Products on Social Media—0
Mixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback—0
Scalable Multi-phase Word Embedding Using Conjunctive Propositional Clauses—0
Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions—0
Israel-Hamas war through Telegram, Reddit and Twitter—0
General Embedding vs. Task-Specific Embedding: A Comparative Approach to Enhancing NLP Performance—0
Semantic Consistency Regularization with Large Language Models for Semi-supervised Sentiment Analysis—0
Experimenting with Affective Computing Models in Video Interviews with Spanish-speaking Older Adults—0
Irony Detection, Reasoning and Understanding in Zero-shot Learning—0
STAR: Stepwise Task Augmentation and Relation Learning for Aspect Sentiment Quad Prediction—0
Multi-View Attention Syntactic Enhanced Graph Convolutional Network for Aspect-based Sentiment AnalysisCode0
Making Sense Of Distributed Representations With Activation Spectroscopy—0
Idiom Detection in Sorani Kurdish Texts—0
Multimodal Stock Price Prediction—0
Document-Level Sentiment Analysis of Urdu Text Using Deep Learning Techniques—0
2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings—0
An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts—0
Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages—0
Multi-Modality Collaborative Learning for Sentiment AnalysisCode1
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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