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 1001–1050 of 5630 papers

TitleStatusHype
Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities—0
Meme Analysis using LLM-based Contextual Information and U-net Encapsulated TransformerCode0
Large Language Models for Judicial Entity Extraction: A Comparative Study—0
Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis—0
New Directions in Text Classification Research: Maximizing The Performance of Sentiment Classification from Limited Data—0
Flood of Techniques and Drought of Theories: Emotion Mining in Disasters—0
Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing—0
Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models—0
Entity-Level Sentiment: More than the Sum of Its PartsCode0
QET: Enhancing Quantized LLM Parameters and KV cache Compression through Element Substitution and Residual Clustering—0
Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation ModelsCode0
Aspect-Based Sentiment Analysis Techniques: A Comparative Study—0
RVISA: Reasoning and Verification for Implicit Sentiment Analysis—0
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks—0
NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP ClassifiersCode0
Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via AdaptersCode0
KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus FilteringCode0
Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis—0
The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models—0
QBI: Quantile-Based Bias Initialization for Efficient Private Data Reconstruction in Federated LearningCode0
Clustering in pure-attention hardmax transformers and its role in sentiment analysisCode0
Application of Liquid Rank Reputation System for Twitter Trend Analysis on Bitcoin—0
Learning in Wilson-Cowan model for metapopulationCode0
A Syntax-Injected Approach for Faster and More Accurate Sentiment AnalysisCode0
Mining United Nations General Assembly DebatesCode0
Comparison of Open-Source and Proprietary LLMs for Machine Reading Comprehension: A Practical Analysis for Industrial Applications—0
Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines—0
Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis—0
Towards Understanding Emotions for Engaged Mental Health Conversations—0
Enhancing Text Classification through LLM-Driven Active Learning and Human AnnotationCode0
Curating Stopwords in Marathi: A TF-IDF Approach for Improved Text Analysis and Information Retrieval—0
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction—0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
Improving Large Models with Small models: Lower Costs and Better PerformanceCode0
Automatically Labeling $200B Life-Saving Datasets: A Large Clinical Trial Outcome Benchmark—0
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey—0
Adversarial Evasion Attack Efficiency against Large Language Models—0
The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor RecognitionCode0
A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles—0
COVID-19 Twitter Sentiment Classification Using Hybrid Deep Learning Model Based on Grid Search Methodology—0
Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanism—0
Improving In-Context Learning with Prediction Feedback for Sentiment AnalysisCode0
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
ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSACode0
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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