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 3601–3650 of 5630 papers

TitleStatusHype
TDParse: Multi-target-specific sentiment recognition on Twitter—0
Teacher-Student Learning Paradigm for Tri-training: An Efficient Method for Unlabeled Data Exploitation—0
Teaching the Basics of NLP and ML in an Introductory Course to Information Science—0
\#TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets—0
Team Kermit-the-frog at SemEval-2019 Task 4: Bias Detection Through Sentiment Analysis and Simple Linguistic Features—0
Team Neuro at SemEval-2020 Task 8: Multi-Modal Fine Grain Emotion Classification of Memes using Multitask Learning—0
TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep Learning—0
TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced Data—0
Teanga: A Linked Data based platform for Natural Language Processing—0
TEASER: Towards Efficient Aspect-based SEntiment Analysis and Recognition—0
Technical Domain Identification using word2vec and BiLSTM—0
Unlocking Criminal Hierarchies: A Survey, Experimental, and Comparative Exploration of Techniques for Identifying Leaders within Criminal Networks—0
TechSSN at SemEval-2022 Task 6: Intended Sarcasm Detection using Transformer Models—0
Tecnolengua Lingmotif at EmoInt-2017: A lexicon-based approach—0
Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions—0
Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE)—0
Tell Model Where to Attend: Improving Interpretability of Aspect-Based Sentiment Classification via Small Explanation Annotations—0
Temperature check: theory and practice for training models with softmax-cross-entropy losses—0
TensiStrength: Stress and relaxation magnitude detection for social media texts—0
Tensor Variable Elimination for Plated Factor Graphs—0
teragram: Rule-based detection of sentiment phrases using SAS Sentiment Analysis—0
Terminology Extraction Approaches for Product Aspect Detection in Customer Reviews—0
Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings—0
Coverage Guided Testing for Recurrent Neural Networks—0
Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models—0
Text2Time: Transformer-based Article Time Period Prediction—0
Text-based Sentiment Analysis and Music Emotion Recognition—0
Text Classification based on Multiple Block Convolutional Highways—0
Text Classification for Azerbaijani Language Using Machine Learning and Embedding—0
Text Classification in the LLM Era - Where do we stand?—0
Text Classification using Graph Convolutional Networks: A Comprehensive Survey—0
Text Compression for Sentiment Analysis via Evolutionary Algorithms—0
TextDecepter: Hard Label Black Box Attack on Text Classification—0
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing—0
TextImager: a Distributed UIMA-based System for NLP—0
Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets—0
TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models—0
Text Revealer: Private Text Reconstruction via Model Inversion Attacks against Transformers—0
Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model—0
Text Sentiment Analysis based on Fusion of Structural Information and Serialization Information—0
TextTN: Probabilistic Encoding of Language on Tensor Network—0
Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations—0
TF-IDFC-RF: A Novel Supervised Term Weighting Scheme—0
TGB at SemEval-2016 Task 5: Multi-Lingual Constraint System for Aspect Based Sentiment Analysis—0
Thai Stock News Sentiment Classification using Wordpair Features—0
The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers—0
The BreakingNews Dataset—0
The Call for Socially Aware Language Technologies—0
thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale—0
The Challenge of Sentiment Quantification—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