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 2951–3000 of 5630 papers

TitleStatusHype
Give your Text Representation Models some Love: the Case for BasqueCode0
Is it feasible to detect FLOSS version release events from textual messages? A case study on Stack Overflow—0
Noisy Text Data: Achilles' Heel of BERT—0
Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification—0
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation—0
Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream TasksCode0
Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network ApproachCode0
Sentiment Analysis in Drug Reviews using Supervised Machine Learning Algorithms—0
A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency—0
Sentiment Analysis with Contextual Embeddings and Self-Attention—0
TF-IDFC-RF: A Novel Supervised Term Weighting Scheme—0
A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis—0
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models—0
Adaptive Name Entity Recognition under Highly Unbalanced Data—0
A Multi-Source Entity-Level Sentiment Corpus for the Financial Domain: The FinLin Corpus—0
Quality of Word Embeddings on Sentiment Analysis Tasks—0
SeMemNN: A Semantic Matrix-Based Memory Neural Network for Text ClassificationCode0
What Emotions Make One or Five Stars? Understanding Ratings of Online Product Reviews by Sentiment Analysis and XAI—0
Word Sense Disambiguation: A comprehensive knowledge exploitation framework—0
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT—0
Gated Mechanism for Attention Based Multimodal Sentiment Analysis—0
Aspect Term Extraction using Graph-based Semi-Supervised Learning—0
A Systematic Comparison of Architectures for Document-Level Sentiment ClassificationCode0
An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies—0
Towards Detection of Subjective Bias using Contextualized Word EmbeddingsCode0
Convolutional Neural Networks for Sentiment Analysis in Persian Social Media—0
Sentiment Analysis Using Averaged Weighted Word Vector FeaturesCode0
Performance Comparison of Crowdworkers and NLP Tools on Named-Entity Recognition and Sentiment Analysis of Political Tweets—0
Description Based Text Classification with Reinforcement Learning—0
Related Tasks can Share! A Multi-task Framework for Affective language—0
GIM: Gaussian Isolation Machines—0
Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case—0
Learning to Detect Malicious Clients for Robust Federated Learning—0
FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications—0
Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification—0
Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device—0
ARAACOM: ARAbic Algerian Corpus for Opinion Mining—0
Emotion and Sentiment Lexicon Impact on Sentiment Analysis Applied to Book Reviews—0
Sequence Labeling Approach to the Task of Sentence Boundary DetectionCode0
Unsupervised Sentiment Analysis for Code-mixed DataCode0
Knowledge Discovery from Social Media using Big Data provided Sentiment Analysis (SoMABiT)—0
A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts—0
Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits—0
Multi-source Domain Adaptation for Visual Sentiment Classification—0
Generating Word and Document Embeddings for Sentiment Analysis—0
Adapting Deep Learning for Sentiment Classification of Code-Switched Informal Short TextCode0
Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data—0
Corpus Based Amharic Sentiment Lexicon Generation—0
Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis—0
Using Extractive Lexicon-based Sentiment Analysis to Enhance Understanding ofthe Impact of Non-GAAP Measures in Financial Reporting—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
2T5-11BAccuracy97.5—Unverified
3MT-DNN-SMARTAccuracy97.5—Unverified
4T5-3BAccuracy97.4—Unverified
5MUPPET Roberta LargeAccuracy97.4—Unverified
6StructBERTRoBERTa ensembleAccuracy97.1—Unverified
7ALBERTAccuracy97.1—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