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 3601–3650 of 5630 papers

TitleStatusHype
Knowledge Discovery from Social Media using Big Data provided Sentiment Analysis (SoMABiT)—0
Knowledge-enriched Two-layered Attention Network for Sentiment Analysis—0
Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features—0
KOSAC: A Full-Fledged Korean Sentiment Analysis Corpus—0
Kotonush: Understanding Concepts Based on Values behind Social Media—0
KU-CST at CoNLL--SIGMORPHON 2018 Shared Task: a Tridirectional Model—0
KUNLPLab:Sentiment Analysis on Twitter Data—0
L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources—0
L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library—0
L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset—0
L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models—0
Label Correction Model for Aspect-based Sentiment Analysis—0
Label Embedding for Zero-shot Fine-grained Named Entity Typing—0
Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection—0
Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification—0
LABR: A Large Scale Arabic Book Reviews Dataset—0
LAMP: Extracting Locally Linear Decision Surfaces from LLM World Models—0
Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English Tweets—0
Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns—0
Language-Agnostic Model for Aspect-Based Sentiment Analysis—0
Language-based game theory in the age of artificial intelligence—0
Language Independent Sentiment Analysis—0
Language-Independent Sentiment Analysis Using Subjectivity and Positional Information—0
Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings—0
Language Independent Sequence Labelling for Opinion Target Extraction—0
Language Model-Driven Data Pruning Enables Efficient Active Learning—0
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data—0
Language Modeling for the Future of Finance: A Quantitative Survey into Metrics, Tasks, and Data Opportunities—0
Language Model Pre-training for Hierarchical Document Representations—0
Language Technology for Agile Social Media Science—0
Large Discourse Treebanks from Scalable Distant Supervision—0
Large Language Model Adaptation for Financial Sentiment Analysis—0
Large Language Model (LLM) Bias Index -- LLMBI—0
Large Language Models' Accuracy in Emulating Human Experts' Evaluation of Public Sentiments about Heated Tobacco Products on Social Media—0
Prompting Large Language Models for Counterfactual Generation: An Empirical Study—0
Large language models for crowd decision making based on prompt design strategies using ChatGPT: models, analysis and challenges—0
Large Language Models for Judicial Entity Extraction: A Comparative Study—0
Large language models in finance : what is financial sentiment?—0
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey—0
Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All—0
Large Scale Analysis of Open MOOC Reviews to Support Learners' Course Selection—0
Large-Scale Goodness Polarity Lexicons for Community Question Answering—0
Large-scale news entity sentiment analysis—0
Large, Small or Both: A Novel Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization—0
Latent Structure Models for Natural Language Processing—0
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity—0
LCCT: A Semi-supervised Model for Sentiment Classification—0
LDCCNLP at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases Using Machine Learning—0
LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony Detection—0
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation—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