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 701–750 of 5630 papers

TitleStatusHype
Idiom Detection in Sorani Kurdish Texts—0
Multimodal Stock Price Prediction—0
Document-Level Sentiment Analysis of Urdu Text Using Deep Learning Techniques—0
2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings—0
Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages—0
An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts—0
Longitudinal Abuse and Sentiment Analysis of Hollywood Movie Dialogues using LLMs—0
Investigating the Impact of Language-Adaptive Fine-Tuning on Sentiment Analysis in Hausa Language Using AfriBERTaCode0
Sympathy over Polarization: A Computational Discourse Analysis of Social Media Posts about the July 2024 Trump Assassination Attempt—0
Sentiment Analysis in Twitter Social Network Centered on Cryptocurrencies Using Machine Learning—0
Expanding Vietnamese SentiWordNet to Improve Performance of Vietnamese Sentiment Analysis Models—0
Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models—0
Reassessing the Role of Chain-of-Thought in Sentiment Analysis: Insights and Limitations—0
Dynamic Multimodal Sentiment Analysis: Leveraging Cross-Modal Attention for Enabled Classification—0
Language Fusion for Parameter-Efficient Cross-lingual TransferCode0
Enhancing Multi-Modal Video Sentiment Classification Through Semi-Supervised Clustering—0
EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses—0
Linguistic Entity Masking to Improve Cross-Lingual Representation of Multilingual Language Models for Low-Resource Languages—0
AgoraSpeech: A multi-annotated comprehensive dataset of political discourse through the lens of humans and AI—0
DisSim-FinBERT: Text Simplification for Core Message Extraction in Complex Financial Texts—0
Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Micro-bloggers—0
HP-BERT: A framework for longitudinal study of Hinduphobia on social media via LLMsCode0
Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis—0
Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model for Metaphor Mapping Identification—0
Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm—0
Towards New Benchmark for AI Alignment & Sentiment Analysis in Socially Important Issues: A Comparative Study of Human and LLMs in the Context of AGI—0
Toward Inclusive Educational AI: Auditing Frontier LLMs through a Multiplexity Lens—0
Embedding-based Approaches to Hyperpartisan News Detection—0
Seek Common Ground While Reserving Differences: Semi-Supervised Image-Text Sentiment Recognition—0
ELECTRA and GPT-4o: Cost-Effective Partners for Sentiment AnalysisCode0
A Multidisciplinary Approach to Telegram Data Analysis—0
HindiLLM: Large Language Model for Hindi—0
"My life is miserable, have to sign 500 autographs everyday": Exposing Humblebragging, the Brags in Disguise—0
Machine Learning for Sentiment Analysis of Imported Food in Trinidad and Tobago—0
SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment AnalysisCode0
Sentiment trading with large language models—0
Research Experiment on Multi-Model Comparison for Chinese Text Classification Tasks—0
Distilling Fine-grained Sentiment Understanding from Large Language ModelsCode0
On the Applicability of Zero-Shot Cross-Lingual Transfer Learning for Sentiment Classification in Distant Language PairsCode0
Multimodal Deep Reinforcement Learning for Portfolio Optimization—0
Three-Class Text Sentiment Analysis Based on LSTM—0
Unlocking Cross-Lingual Sentiment Analysis through Emoji Interpretation: A Multimodal Generative AI ApproachCode0
ERUPD -- English to Roman Urdu Parallel Dataset—0
Robustness of Large Language Models Against Adversarial Attacks—0
COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations—0
DragonVerseQA: Open-Domain Long-Form Context-Aware Question-AnsweringCode0
A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities—0
Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG IntegrationCode0
LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platforms—0
SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems—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