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 1051–1100 of 5630 papers

TitleStatusHype
Combining Argument Mining Techniques—0
Survey on Visual Sentiment Analysis—0
A Survey on Stance Detection for Mis- and Disinformation Identification—0
Analyzing Coreference and Bridging in Product Reviews—0
A Survey on Stance Detection for Mis- and Disinformation Identification—0
A Survey on Sentiment and Emotion Analysis for Computational Literary Studies—0
Analyzing Consumer Reviews for Understanding Drivers of Hotels Ratings: An Indian Perspective—0
Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets—0
Combining Convolution and Recursive Neural Networks for Sentiment Analysis—0
Combining Intra- and Multi-sentential Rhetorical Parsing for Document-level Discourse Analysis—0
A Survey on sentiment analysis in Persian: A Comprehensive System Perspective Covering Challenges and Advances in Resources, and Methods—0
A Survey on Private Transformer Inference—0
Analyst Reports and Stock Performance: Evidence from the Chinese Market—0
A Survey on Aspect-Based Sentiment Classification—0
Analysis of Twitter Data for Postmarketing Surveillance in Pharmacovigilance—0
A Comparison of Indonesia E-Commerce Sentiment Analysis for Marketing Intelligence Effort—0
A Survey of Text Representation Methods and Their Genealogy—0
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models—0
Analysis of Travel Review Data from Reader's Point of View—0
A Survey of Large Language Models for Arabic Language and its Dialects—0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
Analysis of the Fed's communication by using textual entailment model of Zero-Shot classification—0
About Migration Flows and Sentiment Analysis on Twitter data: Building the Bridge between Technical and Legal Approaches to Data Protection—0
A Survey: Credit Sentiment Score Prediction—0
A Supervised Approach for Sentiment Analysis using Skipgrams—0
Advances in Argument Mining—0
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding—0
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis—0
Analysis of opinionated text for opinion mining—0
A Comparison of Domain-based Word Polarity Estimation using different Word Embeddings—0
A study on text-score disagreement in online reviews—0
A study on irony within the context of 7x1-PT corpus—0
A Study on Herd Behavior Using Sentiment Analysis in Online Social Network—0
A Study on Game Review Summarization—0
Analysis of Cross-Institutional Medication Information Annotations in Clinical Notes—0
Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications—0
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis—0
Combining Lexical Features and a Supervised Learning Approach for Arabic Sentiment Analysis—0
Company classification using zero-shot learning—0
A Study of the Effect of Resolving Negation and Sentiment Analysis in Recognizing Text Entailment for Arabic—0
A Study of Suggestions in Opinionated Texts and their Automatic Detection—0
Analysing Russian Trolls via NLP tools—0
A Study of Feature Extraction techniques for Sentiment Analysis—0
Analysing Public Transport User Sentiment on Low Resource Multilingual Data—0
Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention—0
Collocation Polarity Disambiguation Using Web-based Pseudo Contexts—0
A structure-enhanced graph convolutional network for sentiment analysis—0
Analysing Market Sentiments: Utilising Deep Learning to Exploit Relationships within the Economy—0
A Strategy to Combine 1stGen Transformers and Open LLMs for Automatic Text Classification—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