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 5001–5050 of 5630 papers

TitleStatusHype
Class Vectors: Embedding representation of Document Classes—0
CLEAR: Contrastive Learning for Sentence Representation—0
CLex: A Lexicon for Exploring Color, Concept and Emotion Associations in Language—0
ClimaText: A Dataset for Climate Change Topic Detection—0
Closing the Gap: Domain Adaptation from Explicit to Implicit Discourse Relations—0
Cloze-driven Pretraining of Self-attention Networks—0
CLUF: a Neural Model for Second Language Acquisition Modeling—0
CluSent – Combining Semantic Expansion and De-Noising for Dataset-Oriented Sentiment Analysis of Short Texts—0
Cluster-based Prediction of User Ratings for Stylistic Surface Realisation—0
Clustering Aspect-related Phrases by Leveraging Sentiment Distribution Consistency—0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set—0
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations—0
CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning—0
CMUQ@Qatar:Using Rich Lexical Features for Sentiment Analysis on Twitter—0
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities—0
Code Failure Prediction and Pattern Extraction using LSTM Networks—0
Code-Mixed Sentiment Analysis Using Machine Learning and Neural Network Approaches—0
Codeswitching Detection via Lexical Features in Conditional Random Fields—0
CodeX: Combining an SVM Classifier and Character N-gram Language Models for Sentiment Analysis on Twitter Text—0
Coherency Improved Explainable Recommendation via Large Language Model—0
Collecting and Evaluating Lexical Polarity with A Game With a Purpose—0
Collecting Code-Switched Data from Social Media—0
Collective Opinion Target Extraction in Chinese Microblogs—0
Collective Sentiment Classification Based on User Leniency and Product Popularity—0
Collocation Polarity Disambiguation Using Web-based Pseudo Contexts—0
Colloquial Persian POS (CPPOS) Corpus: A Novel Corpus for Colloquial Persian Part of Speech Tagging—0
Columbia NLP: Sentiment Detection of Subjective Phrases in Social Media—0
Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis—0
Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation (invited talk)—0
Combining Argument Mining Techniques—0
Combining Convolution and Recursive Neural Networks for Sentiment Analysis—0
Combining Intra- and Multi-sentential Rhetorical Parsing for Document-level Discourse Analysis—0
Combining Lexical Features and a Supervised Learning Approach for Arabic Sentiment Analysis—0
Combining Minimally-supervised Methods for Arabic Named Entity Recognition—0
Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels—0
Combining Social Cognitive Theories with Linguistic Features for Multi-genre Sentiment Analysis—0
Combining Supervised and Unsupervised Enembles for Knowledge Base Population—0
Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification—0
COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory—0
COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines—0
COMMIT-P1WP3: A Co-occurrence Based Approach to Aspect-Level Sentiment Analysis—0
Commonsense Reasoning for Identifying and Understanding the Implicit Need of Help and Synthesizing Assistive Actions—0
Common Space Embedding of Primal-Dual Relation Semantic Spaces—0
Company classification using zero-shot learning—0
Comparative Analysis of Libraries for the Sentimental Analysis—0
Comparative Analysis of Pooling Mechanisms in LLMs: A Sentiment Analysis Perspective—0
Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages—0
Comparative Opinion Mining: A Review—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