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 2001–2050 of 5630 papers

TitleStatusHype
An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews—0
Estimating the severity of dental and oral problems via sentiment classification over clinical reports—0
Estudo explorat\'orio de categorias gramaticais com potencial de indicadores para a An\'alise de Sentimentos (An Exploratory study of grammatical categories as potential indicators for Sentiment Analysis)[In Portuguese]—0
Ethereum Price Prediction Employing Large Language Models for Short-term and Few-shot Forecasting—0
Distribution of Emotional Reactions to News Articles in Twitter—0
Automatic Sarcasm Detection: A Survey—0
EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods—0
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation—0
Automatic Rule Extraction from Long Short Term Memory Networks—0
Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis—0
An Ensemble Model for Sentiment Analysis of Hindi-English Code-Mixed Data—0
Evaluating Distant Supervision for Subjectivity and Sentiment Analysis on Arabic Twitter Feeds—0
Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification—0
Adverse Media Mining for KYC and ESG Compliance—0
A Concrete Chinese NLP Pipeline—0
Evaluating Gender Bias Transfer from Film Data—0
A Case Study of Chinese Sentiment Analysis on Social Media Reviews Based on LSTM—0
Evaluating Lexical Similarity to build Sentiment Similarity—0
Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis—0
Distributed Representations for Unsupervised Semantic Role Labeling—0
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement—0
Distributed Real-Time Sentiment Analysis for Big Data Social Streams—0
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings—0
Distributed Deep Learning Using Volunteer Computing-Like Paradigm—0
Distinguishing Literal and Non-Literal Usage of German Particle Verbs—0
Evaluating Sentiment Analysis in the Context of Securities Trading—0
Automatic Nostalgia Detection from Bengali Text—0
Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis—0
An Ensemble Method with Sentiment Features and Clustering Support—0
Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers—0
Distinguishing Common and Proper Nouns—0
Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records—0
Automatic Normalization of Word Variations in Code-Mixed Social Media Text—0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Automatic Music Mood Classification of Hindi Songs—0
An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM—0
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives—0
Evaluation of data inconsistency for multi-modal sentiment analysis—0
Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh—0
Evaluation of Domain-specific Word Embeddings using Knowledge Resources—0
Distilling BERT for low complexity network training—0
Evaluation of Non-Negative Matrix Factorization and n-stage Latent Dirichlet Allocation for Emotion Analysis in Turkish Tweets—0
Evaluation of OpenAI o1: Opportunities and Challenges of AGI—0
Automatic Labelling of Topic Models Learned from Twitter by Summarisation—0
Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications—0
Evaluative Pattern Extraction for Automated Text Generation—0
An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies—0
Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance—0
Automatic Identification of Arabic Language Varieties and Dialects in Social Media—0
Distantly Supervised Attribute Detection from Reviews—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
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