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 2701–2750 of 5630 papers

TitleStatusHype
COVID-19Base: A knowledgebase to explore biomedical entities related to COVID-19—0
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning—0
Extracting Aspects and Polarity from Patents—0
In-Context Learning Can Re-learn Forbidden Tasks—0
In-Context Learning for Long-Context Sentiment Analysis on Infrastructure Project Opinions—0
In-Context Learning for Text Classification with Many Labels—0
Causality between Sentiment and Cryptocurrency Prices—0
Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering—0
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis—0
Incorporating Emoji Descriptions Improves Tweet Classification—0
Incorporating End-to-End Speech Recognition Models for Sentiment Analysis—0
COVID-19 sentiment analysis via deep learning during the rise of novel cases—0
Incorporating medical knowledge in BERT for clinical relation extraction—0
Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis—0
Incorporating Relational Knowledge into Word Representations using Subspace Regularization—0
Increasing happiness through conversations with artificial intelligence—0
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing—0
Indian Institute of Technology-Patna: Sentiment Analysis in Twitter—0
Extracting all Aspect-polarity Pairs Jointly in a Text with Relation Extraction Approach—0
Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media—0
Causality Analysis of Twitter Sentiments and Stock Market Returns—0
Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation—0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition—0
Indonesian Social Media Sentiment Analysis With Sarcasm Detection—0
Inducing Domain-specific Noun Polarity Guided by Domain-independent Polarity Preferences of Adjectives—0
Inducing Target-Specific Latent Structures for Aspect Sentiment Classification—0
INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction—0
INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words—0
INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces—0
Creating Domain Dependent Turkish WordNet and SentiNet—0
Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline—0
Inferring Political Preferences from Twitter—0
Creation of Corpus and Analysis in Code-Mixed Kannada-English Social Media Data for POS Tagging—0
Information Space Dashboard—0
Information-theoretic Multi-view Domain Adaptation—0
Are doggies really nicer than dogs? The impact of morphological derivation on emotional valence in German—0
INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines—0
INF-UFRGS-OPINION-MINING at SemEval-2016 Task 6: Automatic Generation of a Training Corpus for Unsupervised Identification of Stance in Tweets—0
INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis—0
INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis—0
AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis—0
InhibiDistilbert: Knowledge Distillation for a ReLU and Addition-based Transformer—0
Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning -- a Case Study on COVID-19—0
Initializing Convolutional Filters with Semantic Features for Text Classification—0
InkubaLM: A small language model for low-resource African languages—0
Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach—0
INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification—0
INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis—0
Insight from NLP Analysis: COVID-19 Vaccines Sentiments on Social Media—0
Investigating Political Herd Mentality: A Community Sentiment Based Approach—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