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
Estimating Reactions and Recommending Products with Generative Models of 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
A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts—0
Explorations in an English Poetry Corpus: A Neurocognitive Poetics Perspective—0
EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods—0
Bridging the gap in online hate speech detection: a comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter—0
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples—0
Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis—0
Evaluating Company-specific Biases in Financial Sentiment Analysis using Large Language Models—0
Evaluating Distant Supervision for Subjectivity and Sentiment Analysis on Arabic Twitter Feeds—0
Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification—0
Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis—0
Exploring Fine-Grained Emotion Detection in Tweets—0
Evaluating Gender Bias Transfer from Film Data—0
Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm—0
Evaluating Lexical Similarity to build Sentiment Similarity—0
Building and Modelling Multilingual Subjective Corpora—0
Evaluating morphological typology in zero-shot cross-lingual transfer—0
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement—0
A Sentiment Analyzer for Hindi Using Hindi Senti Lexicon—0
Evaluating NLP Models via Contrast Sets—0
Evaluating Recurrent Neural Network Explanations—0
Evaluating Sentiment Analysis Evaluation: A Case Study in Securities Trading—0
Evaluating Sentiment Analysis in the Context of Securities Trading—0
Evaluating Sentiment Analysis Systems in Russian—0
Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis—0
Building Chinese Affective Resources in Valence-Arousal Dimensions—0
Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers—0
CodeX: Combining an SVM Classifier and Character N-gram Language Models for Sentiment Analysis on Twitter Text—0
Exploiting Rich Textual User-Product Context for Improving Sentiment Analysis—0
Evaluating Word Embeddings for Indonesian--English Code-Mixed Text Based on Synthetic Data—0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach—0
HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model—0
Evaluation is all you need. Prompting Generative Large Language Models for Annotation Tasks in the Social Sciences. A Primer using Open Models—0
Evaluation of data inconsistency for multi-modal sentiment analysis—0
Codeswitching Detection via Lexical Features in Conditional Random Fields—0
Evaluation of Domain-specific Word Embeddings using Knowledge Resources—0
Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings—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
Building Web-Interfaces for Vector Semantic Models with the WebVectors Toolkit—0
Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications—0
Evaluative Pattern Extraction for Automated Text Generation—0
A Sentiment Analysis of Men’s and Women’s Speech in the BNC64—0
Aligning context-based statistical models of language with brain activity during reading—0
Exploiting Social Network Structure for Person-to-Person Sentiment Analysis—0
A Sentiment Analysis of Medical Text Based on Deep Learning—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