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 1601–1650 of 5630 papers

TitleStatusHype
Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models—0
Developing Language Resources and NLP Tools for the North Korean Language—0
DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach—0
Development of a General Purpose Sentiment Lexicon for Igbo Language—0
Development of a WAZOBIA-Named Entity Recognition System—0
Common Space Embedding of Primal-Dual Relation Semantic Spaces—0
Dialog speech sentiment classification for imbalanced datasets—0
Automatically augmenting an emotion dataset improves classification using audio—0
Did you ever read about Frogs drinking Coffee? Investigating the Compositionality of Multi-Emoji Expressions—0
DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons—0
DIEGOLab: An Approach for Message-level Sentiment Classification in Twitter—0
Different Contexts Lead to Different Word Embeddings—0
Differentiable Window for Dynamic Local Attention—0
An Empirical Study of Benchmarking Chinese Aspect Sentiment Quad Prediction—0
A Survey of Diffusion Models in Natural Language Processing—0
DigNet: Digging Clues from Local-Global Interactive Graph for Aspect-level Sentiment Classification—0
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—0
Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model—0
Automatic Construction of an Annotated Corpus with Implicit Aspects—0
Direct parsing to sentiment graphs—0
Commonsense Reasoning for Identifying and Understanding the Implicit Need of Help and Synthesizing Assistive Actions—0
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability—0
Disambiguating False-Alarm Hashtag Usages in Tweets for Irony Detection—0
Automatic Detection of Point of View Differences in Wikipedia—0
Disconnected Recurrent Neural Networks for Text Categorization—0
Discourse Analysis and Its Applications—0
Discourse Connectors for Latent Subjectivity in Sentiment Analysis—0
Discourse Level Explanatory Relation Extraction from Product Reviews Using First-Order Logic—0
Discourse Parsing with Attention-based Hierarchical Neural Networks—0
Automatic disambiguation of English puns—0
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis—0
Discovering User Interactions in Ideological Discussions—0
Discrete Cosine Transform as Universal Sentence Encoder—0
Automatic evaluation of scientific abstracts through natural language processing—0
COMMIT-P1WP3: A Co-occurrence Based Approach to Aspect-Level Sentiment Analysis—0
Discriminative Models Can Still Outperform Generative Models in Aspect Based Sentiment Analysis—0
Discriminative Models Can Still Outperform Generative Models in Aspect Based Sentiment Analysis—0
Discriminative Models Still Outperform Generative Models in Aspect Based Sentiment Analysis In Cross-Domain and Cross-Lingual Settings—0
Discriminative Neural Sentence Modeling by Tree-Based Convolution—0
COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines—0
Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning—0
Disney at IEST 2018: Predicting Emotions using an Ensemble—0
Dissecting the Practical Lexical Function Model for Compositional Distributional Semantics—0
DisSim-FinBERT: Text Simplification for Core Message Extraction in Complex Financial Texts—0
Distance Based Source Domain Selection for Sentiment Classification—0
Distance Metric Learning for Aspect Phrase Grouping—0
A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content—0
Distantly Supervised Attribute Detection from Reviews—0
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes—0
COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory—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