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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3701–3750 of 5630 papers

TitleStatusHype
The Sentimental Value of Chinese Sub-Character Components—0
The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis—0
The SSIX Corpora: Three Gold Standard Corpora for Sentiment Analysis in English, Spanish and German Financial Microblogs—0
The USAGE review corpus for fine grained multi lingual opinion analysis—0
The use of new technologies to support Public Administration. Sentiment analysis and the case of the app IO—0
The Use of Text Similarity and Sentiment Analysis to Examine Rationales in the Large-Scale Online Deliberations—0
The Users Who Say `Ni': Audience Identification in Chinese-language Restaurant Reviews—0
The Validity of Lexicon-based Sentiment Analysis in Interdisciplinary Research—0
The Web as an Implicit Training Set: Application to Noun Compounds Syntax and Semantics—0
Think Positive: Towards Twitter Sentiment Analysis from Scratch—0
"Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study—0
Three-Class Text Sentiment Analysis Based on LSTM—0
Thumbs Up and Down: Sentiment Analysis of Medical Online Forums—0
Thumbs up? Sentiment Classification using Machine Learning Techniques—0
THU\_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM—0
THU\_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM—0
THU\_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction—0
TI-Capsule: Capsule Network for Stock Exchange Prediction—0
TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems—0
Latency Adjustable Transformer Encoder for Language Understanding—0
Time-aware Personalized Hashtag Recommendation on Social Media—0
Time-Independent and Language-Independent Extraction of Multiword Expressions From Twitter—0
Time-Series Forecasting: Unleashing Long-Term Dependencies with Fractionally Differenced Data—0
Timestamping Documents and Beliefs—0
TJP: Identifying the Polarity of Tweets from Contexts—0
TJP: Using Twitter to Analyze the Polarity of Contexts—0
TJUdeM: A Combination Classifier for Aspect Category Detection and Sentiment Polarity Classification—0
To Annotate or Not? Predicting Performance Drop under Domain Shift—0
Token Masking Improves Transformer-Based Text Classification—0
Topic-Based Chinese Message Polarity Classification System at SIGHAN8-Task2—0
Topic-Based Chinese Message Sentiment Analysis: A Multilayered Analysis System—0
Topic Based Sentiment Analysis Using Deep Learning—0
Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles—0
Topic Detection and Summarization of User Reviews—0
Topic Driven Adaptive Network for Cross-Domain Sentiment Classification—0
Topic Modeling and Sentiment Analysis on Japanese Online Media's Coverage of Nuclear Energy—0
Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction—0
Topic Modeling with Sentiment Clues and Relaxed Labeling Schema—0
Topic-Specific Sentiment Analysis Can Help Identify Political Ideology—0
TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision—0
TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter—0
To the Moon: Analyzing Collective Trading Events on the Wings of Sentiment Analysis—0
To Vaccinate or not to Vaccinate? Analyzing X Power over the Pandemic—0
Toward a Corpus of Cantonese Verbal Comments and their Classification by Multi-dimensional Analysis—0
Toward a unifying model for Opinion, Sentiment and Emotion information extraction—0
Toward Contextual Valence Shifters in Vietnamese Reviews—0
Toward Fine-grained Annotation of Modality in Text—0
Toward Inclusive Educational AI: Auditing Frontier LLMs through a Multiplexity Lens—0
Toward Qualitative Evaluation of Embeddings for Arabic Sentiment Analysis—0
Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning—0
Show:102550
← PrevPage 75 of 113Next →
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