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 301–350 of 5630 papers

TitleStatusHype
Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering—0
Eradicating Social Biases in Sentiment Analysis using Semantic Blinding and Semantic Propagation Graph Neural Networks—0
Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Models—0
MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis—0
CNMBERT: A Model for Converting Hanyu Pinyin Abbreviations to Chinese CharactersCode2
Financial News-Driven LLM Reinforcement Learning for Portfolio Management—0
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
HJ-Ky-0.1: an Evaluation Dataset for Kyrgyz Word EmbeddingsCode1
Gender Bias Mitigation for Bangla Classification TasksCode0
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity—0
On the Cost of Model-Serving Frameworks: An Experimental Evaluation—0
Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid NetworkCode0
The Moral Foundations Weibo Corpus—0
Improvement and Implementation of a Speech Emotion Recognition Model Based on Dual-Layer LSTM—0
Analyst Reports and Stock Performance: Evidence from the Chinese Market—0
EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods—0
IAE: Irony-based Adversarial Examples for Sentiment Analysis Systems—0
Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation—0
CineXDrama: Relevance Detection and Sentiment Analysis of Bangla YouTube Comments on Movie-Drama using Transformers: Insights from Interpretability Tool—0
TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy PreservationCode0
The Empirical Impact of Data Sanitization on Language Models—0
Sentiment Analysis of Cyberbullying Data in Social MediaCode0
What talking you?: Translating Code-Mixed Messaging Texts to EnglishCode0
Words that Move Markets- Quantifying the Impact of RBI's Monetary Policy Communications on Indian Financial Market—0
Sentiment Analysis of Spanish Political Party Tweets Using Pre-trained Language Models—0
FASSILA: A Corpus for Algerian Dialect Fake News Detection and Sentiment AnalysisCode0
Bounded Rationality in Central Bank Communication—0
YouTube Comments Decoded: Leveraging LLMs for Low Resource Language Classification—0
A Multilingual Sentiment Lexicon for Low-Resource Language Translation using Large Languages Models and Explainable AI—0
Prompt Engineering Using GPT for Word-Level Code-Mixed Language Identification in Low-Resource Dravidian Languages—0
Blending Ensemble for Classification with Genetic-algorithm generated Alpha factors and Sentiments (GAS)—0
Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning—0
Explanations that reveal all through the definition of encoding—0
AVSS: Layer Importance Evaluation in Large Language Models via Activation Variance-Sparsity Analysis—0
FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics—0
FEET: A Framework for Evaluating Embedding TechniquesCode0
Interacting Large Language Model Agents. Interpretable Models and Social Learning—0
An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore FrameworkCode0
Evaluating Company-specific Biases in Financial Sentiment Analysis using Large Language Models—0
Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment AnalysisCode0
Narrative Analysis of True Crime Podcasts With Knowledge Graph-Augmented Large Language Models—0
Exploring Vision Language Models for Facial Attribute Recognition: Emotion, Race, Gender, and Age—0
Reducing the Scope of Language Models—0
Leveraging AI and Sentiment Analysis for Forecasting Election Outcomes in Mauritius—0
Personality Analysis from Online Short Video Platforms with Multi-domain AdaptationCode0
Cyberbullying or just Sarcasm? Unmasking Coordinated Networks on Reddit—0
A Survey of Large Language Models for Arabic Language and its Dialects—0
Enhancing Inflation Nowcasting with LLM: Sentiment Analysis on NewsCode0
Sentiment-Driven Community Detection in a Network of Perfume PreferencesCode0
SpeakGer: A meta-data enriched speech corpus of German state and federal parliamentsCode1
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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