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 551–600 of 5630 papers

TitleStatusHype
Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccination—0
A Panoramic Survey of Natural Language Processing in the Arab World—0
A Heterogeneous Graphical Model to Understand User-Level Sentiments in Social Media—0
Any-gram Kernels for Sentence Classification: A Sentiment Analysis Case Study—0
A Helping Hand: Transfer Learning for Deep Sentiment Analysis—0
Active Learning with Transfer Learning—0
OneLove beyond the field -- A few-shot pipeline for topic and sentiment analysis during the FIFA World Cup in Qatar—0
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models—0
An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model—0
``Haters gonna hate'': challenges for sentiment analysis of Facebook comments in Brazilian Portuguese—0
Active Learning Over Multiple Domains in Natural Language Tasks—0
Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference—0
A Novel Way of Identifying Cyber Predators—0
Agreement and Disagreement: Comparison of Points of View in the Political Domain—0
Accountable Error Characterization—0
A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency—0
A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media—0
A Graphical User Interface for Feature-Based Opinion Mining—0
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News—0
AgoraSpeech: A multi-annotated comprehensive dataset of political discourse through the lens of humans and AI—0
Active Learning for Imbalanced Sentiment Classification—0
A Survey of Large Language Models for Arabic Language and its Dialects—0
A Survey of Text Representation Methods and Their Genealogy—0
Survey on Visual Sentiment Analysis—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
A Novel Deep Learning Method for Textual Sentiment Analysis—0
A Gold Standard Dependency Corpus for English—0
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis—0
A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis—0
Active learning for detection of stance components—0
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC—0
A Novel BGCapsule Network for Text Classification—0
Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction—0
A novel Bayesian estimation-based word embedding model for sentiment analysis—0
Agent-Based Simulations of Online Political Discussions: A Case Study on Elections in Germany—0
Active Information Acquisition—0
Accommodations in Tuscany as Linked Data—0
A Supervised Approach for Sentiment Analysis using Skipgrams—0
A novel approach to sentiment analysis in Persian using discourse and external semantic information—0
A Generative Model for Identifying Target Companies of Microblogs—0
A CCG-based Approach to Fine-Grained Sentiment Analysis—0
Anotando um Corpus de Not\' para a An\'alise de Sentimentos: um Relato de Experi\^encia (Annotating a corpus of News for Sentiment Analysis: An Experience Report)—0
An opinion about opinions about opinions: subjectivity and the aggregate reader—0
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis—0
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis—0
A Cross-Validation Study of Turkish Sentiment Analysis Datasets and Tools—0
Annotation Scheme for Constructing Sentiment Corpus in Korean—0
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus—0
A Generalised Hybrid Architecture for NLP—0
Fine-tuning multilingual language models in Twitter/X sentiment analysis: a study on Eastern-European V4 languages—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
6ALBERTAccuracy97.1—Unverified
7StructBERTRoBERTa ensembleAccuracy97.1—Unverified
8XLNet (single model)Accuracy97—Unverified
9RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.9—Unverified
10SMARTRoBERTaDev Accuracy96.9—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