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

Showing 42514300 of 5630 papers

TitleStatusHype
Comparison of String Similarity Measures for Obscenity Filtering0
Creating and Validating Multilingual Semantic Representations for Six Languages: Expert versus Non-Expert Crowds0
Sentiment Analysis of Tunisian Dialects: Linguistic Ressources and ExperimentsCode0
Audience Segmentation in Social Media0
Building Web-Interfaces for Vector Semantic Models with the WebVectors Toolkit0
Lingmotif: Sentiment Analysis for the Digital Humanities0
Attention Modeling for Targeted Sentiment0
Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering0
Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm0
BabelDomains: Large-Scale Domain Labeling of Lexical Resources0
A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content0
TDParse: Multi-target-specific sentiment recognition on Twitter0
The Content Types Dataset: a New Resource to Explore Semantic and Functional Characteristics of Texts0
A Multi-task Approach to Predict Likability of Books0
Multilingual Training of Crosslingual Word Embeddings0
A Multi-View Sentiment Corpus0
Stance Classification of Context-Dependent Claims0
Robust Training under Linguistic AdversityCode0
Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study0
Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets0
Learning to Generate Product Reviews from Attributes0
Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis0
Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications0
Sentiment Analysis of Citations Using Word2vecCode0
Opinion Mining on Non-English Short Text0
Sentiment Recognition in Egocentric Photostreams0
A Tidy Data Model for Natural Language Processing using cleanNLPCode0
Emergence of Grounded Compositional Language in Multi-Agent PopulationsCode1
A Structured Self-attentive Sentence EmbeddingCode1
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment ClassificationCode0
Central Moment Discrepancy (CMD) for Domain-Invariant Representation LearningCode0
Asymmetric Tri-training for Unsupervised Domain AdaptationCode0
Explicit Document Modeling through Weighted Multiple-Instance LearningCode0
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study0
Arabic Language Sentiment Analysis on Health Services0
Data Selection Strategies for Multi-Domain Sentiment AnalysisCode0
Automatic Rule Extraction from Long Short Term Memory Networks0
Knowledge Adaptation: Teaching to Adapt0
EliXa: A Modular and Flexible ABSA Platform0
Q-WordNet PPV: Simple, Robust and (almost) Unsupervised Generation of Polarity Lexicons for Multiple Languages0
Multi-task memory networks for category-specific aspect and opinion terms co-extraction0
All-but-the-Top: Simple and Effective Postprocessing for Word RepresentationsCode0
Sentiment Analysis of Arabic Tweets Using Semantic Resources0
Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling0
Leveraging Cognitive Features for Sentiment Analysis0
Harnessing Cognitive Features for Sarcasm Detection0
SMARTies: Sentiment Models for Arabic Target Entities0
Efficient Twitter Sentiment Classification using Subjective Distant Supervision0
Structural Attention Neural Networks for improved sentiment analysis0
Group Visual Sentiment Analysis0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100Unverified
2MT-DNN-SMARTAccuracy97.5Unverified
3T5-11BAccuracy97.5Unverified
4MUPPET Roberta LargeAccuracy97.4Unverified
5T5-3BAccuracy97.4Unverified
6ALBERTAccuracy97.1Unverified
7StructBERTRoBERTa ensembleAccuracy97.1Unverified
8XLNet (single model)Accuracy97Unverified
9SMARTRoBERTaDev Accuracy96.9Unverified
10ELECTRAAccuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large with LlamBERTAccuracy96.68Unverified
2RoBERTa-largeAccuracy96.54Unverified
3XLNetAccuracy96.21Unverified
4Heinsen Routing + RoBERTa LargeAccuracy96.2Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.1Unverified
6GraphStarAccuracy96Unverified
7DV-ngrams-cosine with NB sub-sampling + RoBERTa.baseAccuracy95.94Unverified
8DV-ngrams-cosine + RoBERTa.baseAccuracy95.92Unverified
9Roberta_Large ST + Cosine Similarity LossAccuracy95.9Unverified
10BERT large finetune UDAAccuracy95.8Unverified
#ModelMetricClaimedVerifiedStatus
1Llama-3.3-70B + CAPOAccuracy62.27Unverified
2Mistral-Small-24B + CAPOAccuracy 60.2Unverified
3Heinsen Routing + RoBERTa LargeAccuracy59.8Unverified
4RoBERTa-large+Self-ExplainingAccuracy59.1Unverified
5Qwen2.5-32B + CAPOAccuracy 59.07Unverified
6Heinsen Routing + GPT-2Accuracy58.5Unverified
7BCN+Suffix BiLSTM-Tied+CoVeAccuracy56.2Unverified
8BERT LargeAccuracy55.5Unverified
9LM-CPPF RoBERTa-baseAccuracy54.9Unverified
10BCN+ELMoAccuracy54.7Unverified
#ModelMetricClaimedVerifiedStatus
1Char-level CNNError4.88Unverified
2SVDCNNError4.74Unverified
3LEAMError4.69Unverified
4fastText, h=10, bigramError4.3Unverified
5SWEM-hierError4.19Unverified
6SRNNError3.96Unverified
7M-ACNNError3.89Unverified
8DNC+CUWError3.6Unverified
9CCCapsNetError3.52Unverified
10Block-sparse LSTMError3.27Unverified
#ModelMetricClaimedVerifiedStatus
1Millions of EmojiTraining Time1,500Unverified
2VLAWEAccuracy93.3Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy92.5Unverified
4AnglE-LLaMA-7BAccuracy91.09Unverified
5byte mLSTM7Accuracy86.8Unverified
6MEANAccuracy84.5Unverified
7RNN-CapsuleAccuracy83.8Unverified
8Capsule-BAccuracy82.3Unverified
9SuBiLSTM-TiedAccuracy81.6Unverified
10USE_T+CNNAccuracy81.59Unverified