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 4651–4700 of 5630 papers

TitleStatusHype
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree—0
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network—0
Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning—0
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network—0
Aspect Sentiment Classification with both Word-level and Clause-level AttentionNetworks—0
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling—0
Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining—0
Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network—0
Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method—0
Aspect Term Extraction using Graph-based Semi-Supervised Learning—0
Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks—0
Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis—0
Assessing Objective Recommendation Quality through Political Forecasting—0
Assessing Sentiment Strength in Words Prior Polarities—0
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets—0
Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction—0
Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods—0
Assessment of Massively Multilingual Sentiment Classifiers—0
Assigning Connotation Values to Events—0
A Statistical Parsing Framework for Sentiment Classification—0
A Stochastic Time Series Model for Predicting Financial Trends using NLP—0
A Strategy to Combine 1stGen Transformers and Open LLMs for Automatic Text Classification—0
A structure-enhanced graph convolutional network for sentiment analysis—0
A Study of Feature Extraction techniques for Sentiment Analysis—0
A Study of Suggestions in Opinionated Texts and their Automatic Detection—0
A Study of the Effect of Resolving Negation and Sentiment Analysis in Recognizing Text Entailment for Arabic—0
A Study on Game Review Summarization—0
A Study on Herd Behavior Using Sentiment Analysis in Online Social Network—0
A study on irony within the context of 7x1-PT corpus—0
A study on text-score disagreement in online reviews—0
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis—0
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding—0
A Supervised Approach for Sentiment Analysis using Skipgrams—0
A Survey: Credit Sentiment Score Prediction—0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
A Survey of Large Language Models for Arabic Language and its Dialects—0
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models—0
A Survey of Text Representation Methods and Their Genealogy—0
A Survey on Aspect-Based Sentiment Classification—0
A Survey on Private Transformer Inference—0
A Survey on sentiment analysis in Persian: A Comprehensive System Perspective Covering Challenges and Advances in Resources, and Methods—0
A Survey on Sentiment and Emotion Analysis for Computational Literary Studies—0
A Survey on Stance Detection for Mis- and Disinformation Identification—0
A Survey on Stance Detection for Mis- and Disinformation Identification—0
Survey on Visual Sentiment Analysis—0
ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques—0
A Systematic Analysis on the Temporal Generalization of Language Models in Social Media—0
A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends—0
A systematic review of early warning systems in finance—0
A System for Extracting Sentiment from Large-Scale Arabic Social Data—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