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 701–750 of 5630 papers

TitleStatusHype
FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment AnalysisCode0
LLMs for Targeted Sentiment in News Headlines: Exploring the Descriptive-Prescriptive Dilemma—0
LLMs in Political Science: Heralding a New Era of Visual Analysis—0
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis—0
Automatic Nostalgia Detection from Bengali Text—0
ESG Sentiment Analysis: comparing human and language model performance including GPT—0
Gradient-Guided Modality Decoupling for Missing-Modality RobustnessCode1
Generating Effective Ensembles for Sentiment Analysis—0
A Machine Learning Approach to Detect Customer Satisfaction From Multiple Tweet Parameters—0
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion ModelingCode0
Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations—0
Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction—0
CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean—0
Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion ModelsCode1
ArabianGPT: Native Arabic GPT-based Large Language Model—0
Exploring and Applying Audio-Based Sentiment Analysis in MusicCode0
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis—0
LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey—0
From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter—0
Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis—0
Learning to Poison Large Language Models for Downstream ManipulationCode1
LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual LexiconsCode1
Applying News and Media Sentiment Analysis for Generating Forex Trading Signals—0
Emoji Driven Crypto Assets Market Reactions—0
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models—0
Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies—0
Do Weibo platform experts perform better at predicting stock market?—0
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensembleCode0
Extensible Multi-Granularity Fusion Network for Aspect-based Sentiment AnalysisCode0
Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image DataCode0
FaBERT: Pre-training BERT on Persian Blogs—0
In-Context Learning Can Re-learn Forbidden Tasks—0
Efficient Models for the Detection of Hate, Abuse and Profanity—0
EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMsCode1
Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses—0
Sentiment-enhanced Graph-based Sarcasm Explanation in DialogueCode0
Homograph Attacks on Maghreb Sentiment Analyzers—0
SIDU-TXT: An XAI Algorithm for NLP with a Holistic Assessment Approach—0
Linguistic features for sentence difficulty prediction in ABSA—0
Comparison of Topic Modelling Approaches in the Banking Context—0
Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon—0
Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN—0
Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network—0
Learning the Market: Sentiment-Based Ensemble Trading Agents—0
PICS: Pipeline for Image Captioning and Search—0
An Information-Theoretic Approach to Analyze NLP Classification TasksCode0
SNNLP: Energy-Efficient Natural Language Processing Using Spiking Neural NetworksCode0
Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks—0
Arabic Tweet Act: A Weighted Ensemble Pre-Trained Transformer Model for Classifying Arabic Speech Acts on Twitter—0
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMsCode0
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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