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 751–800 of 5630 papers

TitleStatusHype
CERM: Context-aware Literature-based Discovery via Sentiment Analysis—0
Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocks—0
Unlocking Criminal Hierarchies: A Survey, Experimental, and Comparative Exploration of Techniques for Identifying Leaders within Criminal Networks—0
Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums—0
Large Language Model Adaptation for Financial Sentiment Analysis—0
ChatGPT vs Gemini vs LLaMA on Multilingual Sentiment Analysis—0
Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods—0
RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models via RomanizationCode0
A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla TextsCode0
A Comprehensive View of the Biases of Toxicity and Sentiment Analysis Methods Towards Utterances with African American English Expressions—0
Longitudinal Sentiment Classification of Reddit Posts—0
Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language ModelsCode0
Confidence Preservation Property in Knowledge Distillation Abstractions—0
Toward Robust Multimodal Learning using Multimodal Foundational Models—0
BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment of Press Releases and Financial Text Around Inflection Points of Biotech Stocks—0
The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases—0
Evolutionary Multi-Objective Optimization of Large Language Model Prompts for Balancing Sentiments—0
Estimating the severity of dental and oral problems via sentiment classification over clinical reports—0
Explain Thyself Bully: Sentiment Aided Cyberbullying Detection with ExplanationCode0
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective AnalysisCode2
Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis—0
Stability Analysis of ChatGPT-based Sentiment Analysis in AI Quality Assurance—0
Milestones in Bengali Sentiment Analysis leveraging Transformer-models: Fundamentals, Challenges and Future Directions—0
Are self-explanations from Large Language Models faithful?Code1
SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERTCode0
Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought—0
WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge—0
Adaptive Data Augmentation for Aspect Sentiment Quad PredictionCode0
Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis—0
Designing Heterogeneous LLM Agents for Financial Sentiment Analysis—0
Natural Language Processing for Dialects of a Language: A Survey—0
Pre-trained Large Language Models for Financial Sentiment AnalysisCode1
We Need to Talk About Classification Evaluation Metrics in NLP—0
Unveiling Comparative Sentiments in Vietnamese Product Reviews: A Sequential Classification FrameworkCode0
Contextual Augmented Global Contrast for Multimodal Intent Recognition—0
MART: Masked Affective RepresenTation Learning via Masked Temporal Distribution Distillation—0
Fine-tuning and Utilization Methods of Domain-specific LLMs—0
Large language model for Bible sentiment analysis: Sermon on the MountCode0
Kernel Density Estimation for Multiclass Quantification—0
Evaluation is all you need. Prompting Generative Large Language Models for Annotation Tasks in the Social Sciences. A Primer using Open Models—0
FABSA: An aspect-based sentiment analysis dataset of user reviewsCode0
Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach—0
Hiding in Plain Sight: Towards the Science of Linguistic Steganography—0
Aspect category learning and sentimental analysis using weakly supervised learning—0
emotion2vec: Self-Supervised Pre-Training for Speech Emotion RepresentationCode3
Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue—0
Large Language Model (LLM) Bias Index -- LLMBI—0
On Quantifying Sentiments of Financial News -- Are We Doing the Right Things?—0
Exploiting Contextual Target Attributes for Target Sentiment Classification—0
Explainable Multimodal Sentiment Analysis on Bengali Memes—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