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 3551–3600 of 5630 papers

TitleStatusHype
Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse DomainsCode0
Learning Sentiment Composition from Sentiment Lexicons—0
A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification—0
Deep Bayesian Learning and Understanding—0
An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing—0
The Other Side of the Coin: Unsupervised Disambiguation of Potentially Idiomatic Expressions by Contrasting Senses—0
Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall RatingsCode0
Expressively vulgar: The socio-dynamics of vulgarity and its effects on sentiment analysis in social mediaCode0
Model-Free Context-Aware Word Composition—0
Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for GermanCode0
Learning Semantic Sentence Embeddings using Sequential Pair-wise DiscriminatorCode0
Simple Algorithms For Sentiment Analysis On Sentiment Rich, Data Poor Domains.—0
NLP for Conversations: Sentiment, Summarization, and Group Dynamics—0
Learning Word Meta-Embeddings by AutoencodingCode0
Aspect-based summarization of pros and cons in unstructured product reviewsCode0
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis—0
Aspect and Sentiment Aware Abstractive Review Summarization—0
Hybrid Attention based Multimodal Network for Spoken Language Classification—0
Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew—0
Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering—0
Transfer Learning for Entity Recognition of Novel ClassesCode0
YouTube AV 50K: An Annotated Corpus for Comments in Autonomous VehiclesCode0
Learning low dimensional word based linear classifiers using Data Shared Adaptive Bootstrap Aggregated Lasso with application to IMDb data—0
Text Classification based on Multiple Block Convolutional Highways—0
Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM—0
Twitter Sentiment Analysis System—0
Concept-Based Embeddings for Natural Language Processing—0
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification—0
Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis—0
Towards Enhancing Lexical Resource and Using Sense-annotations of OntoSenseNet for Sentiment AnalysisCode0
A Combined CNN and LSTM Model for Arabic Sentiment Analysis—0
Natural Language Processing for Music Knowledge DiscoveryCode0
Sliced Recurrent Neural NetworksCode0
A Review of Different Word Embeddings for Sentiment Classification using Deep LearningCode0
Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media—0
BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations—0
A Convolutional Neural Network for Aspect Sentiment Classification—0
Polarity and Intensity: the Two Aspects of Sentiment Analysis—0
Getting the subtext without the text: Scalable multimodal sentiment classification from visual and acoustic modalities—0
Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion LexiconsCode0
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph—0
NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation—0
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier—0
Semantically Equivalent Adversarial Rules for Debugging NLP modelsCode0
Leveraging News Sentiment to Improve Microblog Sentiment Classification in the Financial Domain—0
Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning—0
Pretraining Sentiment Classifiers with Unlabeled Dialog Data—0
Causality Analysis of Twitter Sentiments and Stock Market Returns—0
Beyond Multiword Expressions: Processing Idioms and Metaphors—0
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement—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