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 3101–3150 of 5630 papers

TitleStatusHype
Financial Keyword Expansion via Continuous Word Vector Representations—0
Financial News-Driven LLM Reinforcement Learning for Portfolio Management—0
Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets—0
Financial Sentiment Analysis for Risk Prediction—0
Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT—0
Financial sentiment analysis using FinBERT with application in predicting stock movement—0
FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models—0
FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics—0
FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis—0
Finding fake reviews in e-commerce platforms by using hybrid algorithms—0
Finding Opinion Manipulation Trolls in News Community Forums—0
Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text—0
Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages—0
Finding the Needle in a Haystack: Unsupervised Rationale Extraction from Long Text Classifiers—0
Fine-grained Affective Processing Capabilities Emerging from Large Language Models—0
Fine-Grained Arabic Dialect Identification—0
Fine-Grained Contextual Predictions for Hard Sentiment Words—0
Fine-grained Financial Opinion Mining: A Survey and Research Agenda—0
Fine-grained German Sentiment Analysis on Social Media—0
Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity—0
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings—0
Fine-Grained Opinion Summarization with Minimal Supervision—0
Fine-Grained Sentiment Analysis for Movie Reviews in Bulgarian—0
Fine-Grained Sentiment Analysis of Electric Vehicle User Reviews: A Bidirectional LSTM Approach to Capturing Emotional Intensity in Chinese Text—0
Fine-grained Sentiment Analysis with Faithful Attention—0
Fine Granular Aspect Analysis using Latent Structural Models—0
Fine-tune BERT with Sparse Self-Attention Mechanism—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
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
Fine-tuning Pretrained Multilingual BERT Model for Indonesian Aspect-based Sentiment Analysis—0
Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks—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
Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis—0
FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications—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
FinSentiA: Sentiment Analysis in English Financial Microblogs—0
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance—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
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
Food-Related Sentiment Analysis for Cantonese—0
FooTweets: A Bilingual Parallel Corpus of World Cup Tweets—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