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 2951–3000 of 5630 papers

TitleStatusHype
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
EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods—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
Evaluating Financial Sentiment Analysis with Annotators Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy—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
Evaluating morphological typology in zero-shot cross-lingual transfer—0
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement—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
Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers—0
Can We Identify Stance Without Target Arguments? A Study for Rumour Stance Classification—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
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
Evaluation of different strategies for domain adaptation in opinion mining—0
Evaluation of Domain-specific Word Embeddings using Knowledge Resources—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
Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications—0
Evaluative Pattern Extraction for Automated Text Generation—0
Event Based Emotion Classification for News Articles—0
Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds—0
Event Role Extraction using Domain-Relevant Word Representations—0
Every Bite Is an Experience: Key Point Analysis of Business Reviews—0
Evidence of disorientation towards immunization on online social media after contrasting political communication on vaccines. Results from an analysis of Twitter data in Italy—0
Evolutionary Multi-Objective Optimization of Large Language Model Prompts for Balancing Sentiments—0
Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from A State in Australia—0
Examining Citations of Natural Language Processing Literature—0
Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework—0
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems—0
ExCode-Mixed: Explainable Approaches towards Sentiment Analysis on Code-Mixed Data using BERT models—0
Executive Voiced Laughter and Social Approval: An Explorative Machine Learning Study—0
Expanding Vietnamese SentiWordNet to Improve Performance of Vietnamese Sentiment Analysis Models—0
Expanding wordnets to new languages with multilingual sense disambiguation—0
Experimental Evaluation of a Lexicon- and Corpus-based Ensemble for Multi-way Sentiment Analysis—0
Experimenting with Affective Computing Models in Video Interviews with Spanish-speaking Older Adults—0
Experimenting with UD Adaptation of an Unsupervised Rule-based Approach for Sentiment Analysis of Mexican Tourist Texts—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