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 501–550 of 5630 papers

TitleStatusHype
Sentiment Analysis of State Bank of Pakistan's Monetary Policy Documents and its Impact on Stock Market—0
BERTer: The Efficient One—0
Prompted Aspect Key Point Analysis for Quantitative Review SummarizationCode0
High Risk of Political Bias in Black Box Emotion Inference Models—0
Dynamic Sentiment Analysis with Local Large Language Models using Majority Voting: A Study on Factors Affecting Restaurant Evaluation—0
Deep Learning-based Sentiment Analysis of Olympics Tweets—0
Boosting Zero-Shot Crosslingual Performance using LLM-Based Augmentations with Effective Data SelectionCode0
Enhancing Long-Range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-Based Sentiment Analysis—0
Nullpointer at CheckThat! 2024: Identifying Subjectivity from Multilingual Text SequenceCode0
Tamil Language Computing: the Present and the Future—0
The Language of Weather: Social Media Reactions to Weather Accounting for Climatic and Linguistic Baselines—0
Multi-task Prompt Words Learning for Social Media Content Generation—0
Identification of emotions on Twitter during the 2022 electoral process in Colombia—0
Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities—0
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis—0
Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis—0
Meme Analysis using LLM-based Contextual Information and U-net Encapsulated TransformerCode0
New Directions in Text Classification Research: Maximizing The Performance of Sentiment Classification from Limited Data—0
Large Language Models for Judicial Entity Extraction: A Comparative Study—0
Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion RecognitionCode2
Flood of Techniques and Drought of Theories: Emotion Mining in Disasters—0
Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing—0
QET: Enhancing Quantized LLM Parameters and KV cache Compression through Element Substitution and Residual Clustering—0
Entity-Level Sentiment: More than the Sum of Its PartsCode0
Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation ModelsCode0
Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models—0
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
Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via AdaptersCode0
NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP ClassifiersCode0
Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis—0
KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus FilteringCode0
The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models—0
Clustering in pure-attention hardmax transformers and its role in sentiment analysisCode0
QBI: Quantile-Based Bias Initialization for Efficient Private Data Reconstruction in Federated LearningCode0
Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad PredictionCode1
Application of Liquid Rank Reputation System for Twitter Trend Analysis on Bitcoin—0
Learning in Wilson-Cowan model for metapopulationCode0
InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm DetectionCode1
Dancing in the syntax forest: fast, accurate and explainable sentiment analysis with SALSACode1
A Syntax-Injected Approach for Faster and More Accurate Sentiment AnalysisCode0
Comparison of Open-Source and Proprietary LLMs for Machine Reading Comprehension: A Practical Analysis for Industrial Applications—0
Mining United Nations General Assembly DebatesCode0
Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines—0
Enhancing Text Classification through LLM-Driven Active Learning and Human AnnotationCode0
Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis—0
Towards Understanding Emotions for Engaged Mental Health Conversations—0
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
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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
2T5-11BAccuracy97.5—Unverified
3MT-DNN-SMARTAccuracy97.5—Unverified
4T5-3BAccuracy97.4—Unverified
5MUPPET Roberta LargeAccuracy97.4—Unverified
6StructBERTRoBERTa ensembleAccuracy97.1—Unverified
7ALBERTAccuracy97.1—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