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 3951–4000 of 5630 papers

TitleStatusHype
UserAdapter: Few-Shot User Learning in Sentiment Analysis—0
User Based Aggregation for Biterm Topic Model—0
User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models—0
Noisy Text Data: Achilles' Heel of BERT—0
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis—0
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis—0
USF: Chunking for Aspect-term Identification \& Polarity Classification—0
USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders—0
USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection—0
Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia—0
Using Combined Lexical Resources to Identify Hashtag Types—0
Using complex prompts to identify fine-grained biases in image generation through ChatGPT-4o—0
Using Contextually Aligned Online Reviews to Measure LLMs' Performance Disparities Across Language Varieties—0
Using Convolutional Neural Networks to Classify Hate-Speech—0
Using Crowdsourcing to get Representations based on Regular Expressions—0
Using Extractive Lexicon-based Sentiment Analysis to Enhance Understanding ofthe Impact of Non-GAAP Measures in Financial Reporting—0
Using GAN-based models to sentimental analysis on imbalanced datasets in education domain—0
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms—0
Using Hadoop for Large Scale Analysis on Twitter: A Technical Report—0
Using LDA and LSTM Models to Study Public Opinions and Critical Groups Towards Congestion Pricing in New York City through 2007 to 2019—0
Using lexical level information in discourse structures for Basque sentiment analysis—0
Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Micro-bloggers—0
Using Maximum Entropy Models to Discriminate between Similar Languages and Varieties—0
Using objective words in the reviews to improve the colloquial arabic sentiment analysis—0
Using Pivot-Based Paraphrasing and Sentiment Profiles to Improve a Subjectivity Lexicon for Essay Data—0
Using Psuedolabels for training Sentiment Classifiers makes the model generalize better across datasets—0
Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment Analysis Models—0
Using Recurrent Neural Network for Learning Expressive Ontologies—0
Using Selective Masking as a Bridge between Pre-training and Fine-tuning—0
Using Self-Organizing Maps for Sentiment Analysis—0
Using Skipgrams, Bigrams, and Part of Speech Features for Sentiment Classification of Twitter Messages—0
Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in Customer Service Conversations—0
Using the Past Knowledge to Improve Sentiment Classification—0
Using Twitter and Sentiment Analysis for event detection—0
Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Social Media Product Reviews—0
Using Word Embeddings in Twitter Election Classification—0
Using Word Embedding to Reveal Monetary Policy Explanation Changes—0
USNA: A Dual-Classifier Approach to Contextual Sentiment Analysis—0
UT-DB: An Experimental Study on Sentiment Analysis in Twitter—0
Utilizing distilBert transformer model for sentiment classification of COVID-19's Persian open-text responses—0
Utilizing Large Language Models for Event Deconstruction to Enhance Multimodal Aspect-Based Sentiment Analysis—0
Utilizing Large Twitter Corpora to Create Sentiment Lexica—0
Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysis—0
Utilizing Weak Supervision To Generate Indonesian Conservation Dataset—0
Learning Robust Heterogeneous Signal Features from Parallel Neural Network for Audio Sentiment Analysis—0
Utterance-Level Multimodal Sentiment Analysis—0
UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis—0
UWAV at SemEval-2017 Task 7: Automated feature-based system for locating puns—0
UWB at IEST 2018: Emotion Prediction in Tweets with Bidirectional Long Short-Term Memory Neural Network—0
UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis—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