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 26012650 of 5630 papers

TitleStatusHype
Visual Sentiment Analysis from Disaster Images in Social Media0
Aspect-Based Sentiment Analysis Based on BERT-DAOA0
Sentiment Analysis for Investment Atmosphere Scoring0
LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment AnalysisCode0
A Decade of In-text Citation Analysis based on Natural Language Processing and Machine Learning Techniques: An overview of empirical studies0
Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic0
SHAP values for Explaining CNN-based Text Classification Models0
Decision Tree J48 at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text (Hinglish)0
Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label ImbalanceCode0
The Impact of Indirect Machine Translation on Sentiment Classification0
Simple Unsupervised Similarity-Based Aspect ExtractionCode0
Many-to-one Recurrent Neural Network for Session-based Recommendation0
Learning from students' perception on professors through opinion mining0
Two Stages Approach for Tweet Engagement Prediction0
Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning -- a Case Study on COVID-190
HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment DetectionCode1
Turkish Text Classification: From Lexicon Analysis to Bidirectional Transformer0
A Variational Approach to Unsupervised Sentiment Analysis0
SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality0
TextDecepter: Hard Label Black Box Attack on Text ClassifiersCode0
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification0
Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion RecognitionCode1
Feature Extraction Functions for Neural Logic Rule Learning0
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP ModelsCode2
Modeling Inter-Aspect Dependencies with a Non-temporal Mechanism for Aspect-Based Sentiment Analysis0
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word EmbeddingsCode0
On Commonsense Cues in BERT for Solving Commonsense Tasks0
SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets0
C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering0
A Context-based Disambiguation Model for Sentiment Concepts Using a Bag-of-concepts Approach0
TPFN: Applying Outer Product along Time to Multimodal Sentiment Analysis Fusion on Incomplete Data0
Using LDA and LSTM Models to Study Public Opinions and Critical Groups Towards Congestion Pricing in New York City through 2007 to 20190
Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology using Natural Language Processing and Deep Learning for Smarter Decision Aid. Case study of restaurant choice using TripAdvisor reviewsCode0
A Study of fastText Word Embedding Effects in Document Classification in Bangla LanguageCode0
Deep Learning Brasil -- NLP at SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets0
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion AnalysisCode0
Improving Results on Russian Sentiment DatasetsCode0
Preparation of Sentiment tagged Parallel Corpus and Testing its effect on Machine Translation0
ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text0
Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis0
Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding0
IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text using Deep Neural Networks and Linear Baselines0
JUNLP@SemEval-2020 Task 9:Sentiment Analysis of Hindi-English code mixed data using Grid Search Cross Validation0
FiSSA at SemEval-2020 Task 9: Fine-tuned For FeelingsCode0
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News0
NITS-Hinglish-SentiMix at SemEval-2020 Task 9: Sentiment Analysis For Code-Mixed Social Media Text Using an Ensemble Model0
HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed TextsCode0
BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed TextCode1
Inferring Political Preferences from Twitter0
IITK at SemEval-2020 Task 8: Unimodal and Bimodal Sentiment Analysis of Internet MemesCode1
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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