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 3451–3500 of 5630 papers

TitleStatusHype
Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality—0
Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition—0
Some Theoretical Considerations in Off-the-Shelf Text Analysis Software—0
“Something Something Hota Hai!” An Explainable Approach towards Sentiment Analysis on Indian Code-Mixed Data—0
SoochowDS at ROCLING-2021 Shared Task: Text Sentiment Analysis Using BERT and LSTM—0
Span Detection for Vietnamese Aspect-Based Sentiment Analysis—0
Spanish DAL: A Spanish Dictionary of Affect in Language—0
SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction—0
SPARSAR: An Expressive Poetry Reader—0
Sparse Fuzzy Attention for Structured Sentiment Analysis—0
SPARSE: Structured Prediction using Argument-Relative Structured Encoding—0
SPDB Innovation Lab at SemEval-2022 Task 10: A Novel End-to-End Structured Sentiment Analysis Model based on the ERNIE-M—0
Specializing Word Embeddings for Similarity or Relatedness—0
Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models—0
Spherical Paragraph Model—0
Spinning Sequence-to-Sequence Models with Meta-Backdoors—0
Splusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of Tweets—0
Squared English Word: A Method of Generating Glyph to Use Super Characters for Sentiment Analysis—0
Squibs: Evaluation Methods for Statistically Dependent Text—0
SSA-UO: Unsupervised Sentiment Analysis in Twitter—0
S-Sense: A Sentiment Analysis Framework for Social Media Sensing—0
SSN\_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier—0
SSN\_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model—0
SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection—0
SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron—0
SSN_MLRG1 at SemEval-2022 Task 10: Structured Sentiment Analysis using 2-layer BiLSTM—0
SSP-Based Construction of Evaluation-Annotated Data for Fine-Grained Aspect-Based Sentiment Analysis—0
Stability Analysis of ChatGPT-based Sentiment Analysis in AI Quality Assurance—0
Stacked Ensembles of Information Extractors for Knowledge-Base Population—0
Stacking with Neural network for Cryptocurrency investment—0
Robust Stance Detection: Understanding Public Perceptions in Social Media—0
Stance Classification in Online Debates by Recognizing Users' Intentions—0
Stance Classification of Context-Dependent Claims—0
Stance Detection in Turkish Tweets—0
Stance Detection on Social Media: State of the Art and Trends—0
Stance Detection on Tweets: An SVM-based Approach—0
Standardisation of Dialect Comments in Social Networks in View of Sentiment Analysis : Case of Tunisian Dialect—0
STAR: Stepwise Task Augmentation and Relation Learning for Aspect Sentiment Quad Prediction—0
State of NLP in Kenya: A Survey—0
State-Regularized Recurrent Neural Networks—0
State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions—0
Statistical Mechanical Analysis of Semantic Orientations on Lexical Network—0
SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification—0
Steps Toward Automatic Understanding of the Function of Affective Language in Support Groups—0
Stochastic Tokenization with a Language Model for Neural Text Classification—0
Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method—0
Stock Market Sentiment Classification and Backtesting via Fine-tuned BERT—0
Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanism—0
Stock Price Prediction Under Anomalous Circumstances—0
Stock price prediction using BERT and GAN—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