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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2751–2800 of 5630 papers

TitleStatusHype
A Deep Learning System for Sentiment Analysis of Service Calls—0
A deep-learning framework to detect sarcasm targets—0
A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts—0
Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models—0
Examining Structure of Word Embeddings with PCA—0
Analysis of Chinese Tourists in Japan by Text Mining of a Hotel Portal Site—0
I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter—0
Integrating Emotion Distribution Networks and Textual Message Analysis for X User Emotional State Classification—0
Integration of Lexical and Semantic Knowledge for Sentiment Analysis in SMS—0
Intelligent Analyses on Storytelling for Impact Measurement—0
Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification—0
Intention Analysis for Sales, Marketing and Customer Service—0
Construction of Vietnamese SentiWordNet by using Vietnamese Dictionary—0
Interactive Annotation for Event Modality in Modern Standard and Egyptian Arabic Tweets—0
Hybrid RNN at SemEval-2019 Task 9: Blending Information Sources for Domain-Independent Suggestion Mining—0
Interactive Reinforcement Learning for Table Balancing Robot—0
Hybrid Quantum-Classical Machine Learning for Sentiment Analysis—0
Construction of Emotional Lexicon Using Potts Model—0
Aspect-Based Sentiment Analysis Using Bitmask Bidirectional Long Short Term Memory Networks—0
Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates—0
Interpretable Emoji Prediction via Label-Wise Attention LSTMs—0
Hybrid Models for Lexical Acquisition of Correlated Styles—0
Hybrid Method of Semi-supervised Learning and Feature Weighted Learning for Domain Adaptation of Document Classification—0
InterpreT: An Interactive Visualization Tool for Interpreting Transformers—0
Aspect-Based Sentiment Analysis using BERT—0
Interpretation of NLP models through input marginalization—0
A Multi-Agent Probabilistic Inference Framework Inspired by Kairanban-Style CoT System with IdoBata Conversation for Debiasing—0
Hybrid Improved Document-level Embedding (HIDE)—0
Hybrid Emotion Recognition: Enhancing Customer Interactions Through Acoustic and Textual Analysis—0
Hybrid Deep Belief Networks for Semi-supervised Sentiment Classification—0
Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis—0
Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems—0
Hybrid Attention based Multimodal Network for Spoken Language Classification—0
Hybrid approach to detecting symptoms of depression in social media entries—0
Intrinsically Sparse Long Short-Term Memory Networks—0
Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance—0
Introducing A large Tunisian Arabizi Dialectal Dataset for Sentiment Analysis—0
ConnotationWordNet: Learning Connotation over the Word+Sense Network—0
Hybrid Approach for Single Text Document Summarization using Statistical and Sentiment Features—0
Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning—0
Huntsville, hospitals, and hockey teams: Names can reveal your location—0
Connotation Lexicon: A Dash of Sentiment Beneath the Surface Meaning—0
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture—0
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis—0
Investigating Monolingual and Multilingual BERTModels for Vietnamese Aspect Category Detection—0
Investigating Opinion Mining through Language Varieties: a Case Study of Brazilian and European Portuguese tweets—0
Amrita\_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets—0
Investigating Redundancy in Emoji Use: Study on a Twitter Based Corpus—0
A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot—0
Discovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network—0
Show:102550
← PrevPage 56 of 113Next →
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