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 551–600 of 5630 papers

TitleStatusHype
Improving Large Models with Small models: Lower Costs and Better PerformanceCode0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
Automatically Labeling $200B Life-Saving Datasets: A Large Clinical Trial Outcome Benchmark—0
Adversarial Evasion Attack Efficiency against Large Language Models—0
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey—0
COVID-19 Twitter Sentiment Classification Using Hybrid Deep Learning Model Based on Grid Search Methodology—0
A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles—0
The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor RecognitionCode0
fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functionsCode1
Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanism—0
Large Language Models as Evaluators for Recommendation ExplanationsCode1
Improving In-Context Learning with Prediction Feedback for Sentiment AnalysisCode0
S^2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment AnalysisCode1
Why is "Problems" Predictive of Positive Sentiment? A Case Study of Explaining Unintuitive Features in Sentiment Classification—0
Evaluation of data inconsistency for multi-modal sentiment analysis—0
Linguistic Fingerprint in Transformer Models: How Language Variation Influences Parameter Selection in Irony Detection—0
Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels—0
Beyond Metrics: Evaluating LLMs' Effectiveness in Culturally Nuanced, Low-Resource Real-World Scenarios—0
RoBERTa-BiLSTM: A Context-Aware Hybrid Model for Sentiment AnalysisCode0
It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis PerformanceCode0
Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation—0
ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSACode0
A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews—0
Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment AnalysisCode1
PRFashion24: A Dataset for Sentiment Analysis of Fashion Products Reviews in Persian—0
Context is Important in Depressive Language: A Study of the Interaction Between the Sentiments and Linguistic Markers in Reddit DiscussionsCode0
LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models—0
Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer—0
Performance evaluation of Reddit Comments using Machine Learning and Natural Language Processing methods in Sentiment Analysis—0
Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information—0
OptLLM: Optimal Assignment of Queries to Large Language ModelsCode0
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment—0
Exploring Alignment in Shared Cross-lingual SpacesCode0
Generative AI Search Engines as Arbiters of Public Knowledge: An Audit of Bias and Authority—0
Code-mixed Sentiment and Hate-speech PredictionCode0
Large language models for newspaper sentiment analysis during COVID-19: The GuardianCode0
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis—0
Unveiling factors influencing judgment variation in Sentiment Analysis with Natural Language Processing and Statistics—0
Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques—0
Adaptable and Reliable Text Classification using Large Language ModelsCode1
A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text—0
SynthesizRR: Generating Diverse Datasets with Retrieval AugmentationCode1
Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)—0
Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline—0
A Systematic Analysis on the Temporal Generalization of Language Models in Social Media—0
Bridging the gap in online hate speech detection: a comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter—0
Amplifying Aspect-Sentence Awareness: A Novel Approach for Aspect-Based Sentiment Analysis—0
Impact of Stickers on Multimodal Chat Sentiment Analysis and Intent Recognition: A New Task, Dataset and Baseline—0
Understanding Sarcoidosis Using Large Language Models and Social Media Data—0
Deciphering public attention to geoengineering and climate issues using machine learning and dynamic analysisCode0
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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