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 2251–2300 of 5630 papers

TitleStatusHype
Feature based Sentiment Analysis using a Domain Ontology—0
Citation Analysis with Neural Attention Models—0
ARTICLE: Annotator Reliability Through In-Context Learning—0
Fine Granular Aspect Analysis using Latent Structural Models—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
Challenges in modality annotation in a Brazilian Portuguese Spontaneous Speech Corpus—0
Fine-tune BERT with Sparse Self-Attention Mechanism—0
FBM: Combining lexicon-based ML and heuristics for Social Media Polarities—0
Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study—0
Fine-tuning and Utilization Methods of Domain-specific LLMs—0
FBK: Sentiment Analysis in Twitter with Tweetsted—0
Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines—0
Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual Predatory Chats and Abusive Texts—0
Challenges in Creating a Multilingual Sentiment Analysis Application for Social Media Mining—0
Fine-tuning Pretrained Multilingual BERT Model for Indonesian Aspect-based Sentiment Analysis—0
Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks—0
Clarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Review—0
FinGPT: Democratizing Internet-scale Data for Financial Large Language Models—0
FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs—0
FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets—0
A review of sentiment analysis research in Arabic language—0
A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification—0
FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation—0
FINN-GL: Generalized Mixed-Precision Extensions for FPGA-Accelerated LSTMs—0
FinnSentiment -- A Finnish Social Media Corpus for Sentiment Polarity Annotation—0
Geographical Evaluation of Word Embeddings—0
FinSentiA: Sentiment Analysis in English Financial Microblogs—0
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance—0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings—0
FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis—0
Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis—0
FBK: Exploiting Phrasal and Contextual Clues for Negation Scope Detection—0
Challenges for Open-domain Targeted Sentiment Analysis—0
Flood of Techniques and Drought of Theories: Emotion Mining in Disasters—0
Flower Across Time and Media: Sentiment Analysis of Tang Song Poetry and Visual Correspondence—0
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps—0
FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications—0
Food-Related Sentiment Analysis for Cantonese—0
FooTweets: A Bilingual Parallel Corpus of World Cup Tweets—0
Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media—0
Challenges for Open-domain Targeted Sentiment Analysis—0
Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data—0
Forecasting with Economic News—0
Foreign Words and the Automatic Processing of Arabic Social Media Text Written in Roman Script—0
A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences—0
Forward and Backward Knowledge Transfer for Sentiment Classification—0
A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing—0
Class Vectors: Embedding representation of Document Classes—0
Fraunhofer SIT@SMM4H’22: Learning to Predict Stances and Premises in Tweets related to COVID-19 Health Orders Using Generative Models—0
Generative Adversarial Imitation Learning for Empathy-based AI—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