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 451–500 of 5630 papers

TitleStatusHype
Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews—0
Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning—0
Unlearning Trojans in Large Language Models: A Comparison Between Natural Language and Source Code—0
Implicit Sentiment Analysis Based on Chain of Thought Prompting—0
Great Memory, Shallow Reasoning: Limits of kNN-LMsCode1
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding—0
Is the Lecture Engaging for Learning? Lecture Voice Sentiment Analysis for Knowledge Graph-Supported Intelligent Lecturing Assistant (ILA) SystemCode0
A Strategy to Combine 1stGen Transformers and Open LLMs for Automatic Text Classification—0
PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis—0
Sentiment analysis of preservice teachers' reflections using a large language model—0
BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis—0
Quantifying the Effectiveness of Student Organization Activities using Natural Language Processing—0
Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models—0
Rater Cohesion and Quality from a Vicarious PerspectiveCode0
GERestaurant: A German Dataset of Annotated Restaurant Reviews for Aspect-Based Sentiment Analysis—0
Quantum-inspired Interpretable Deep Learning Architecture for Text Sentiment AnalysisCode0
A Deep Features-Based Approach Using Modified ResNet50 and Gradient Boosting for Visual Sentiments Classification—0
Comprehensive dataset of user-submitted articles with ideological and extreme bias from RedditCode0
WiDe-analysis: Enabling One-click Content Moderation Analysis on Wikipedia's Articles for Deletion—0
An Evaluation of Standard Statistical Models and LLMs on Time Series ForecastingCode0
Ensemble BERT: A student social network text sentiment classification model based on ensemble learning and BERT architecture—0
Recognizing Emotion Regulation Strategies from Human Behavior with Large Language Models—0
Enhanced Semantic Graph Based Approach With Sentiment Analysis For User Interest Retrieval From Social Sites—0
Are Social Sentiments Inherent in LLMs? An Empirical Study on Extraction of Inter-demographic Sentiments—0
Analyzing Consumer Reviews for Understanding Drivers of Hotels Ratings: An Indian Perspective—0
Decoding Visual Sentiment of Political Imagery—0
OneLove beyond the field -- A few-shot pipeline for topic and sentiment analysis during the FIFA World Cup in Qatar—0
Fine-tuning multilingual language models in Twitter/X sentiment analysis: a study on Eastern-European V4 languages—0
Optimal and efficient text counterfactuals using Graph Neural NetworksCode0
Efficient Solutions For An Intriguing Failure of LLMs: Long Context Window Does Not Mean LLMs Can Analyze Long Sequences Flawlessly—0
Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis—0
Using LLMs to Establish Implicit User Sentiment of Software Desirability—0
Leveraging Encoder-only Large Language Models for Mobile App Review Feature ExtractionCode0
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 PandemicCode0
Generative Sentiment Analysis via Latent Category Distribution and Constrained Decoding—0
Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion RecognitionCode1
Effective Black Box Testing of Sentiment Analysis Classification Networks—0
A Temporal Psycholinguistics Approach to Identity Resolution of Social Media Users—0
Sentiment Analysis of Lithuanian Online Reviews Using Large Language Models—0
Monetizing Currency Pair Sentiments through LLM ExplainabilityCode0
Motamot: A Dataset for Revealing the Supremacy of Large Language Models over Transformer Models in Bengali Political Sentiment AnalysisCode0
A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention—0
RoBERTa, ResNeXt and BiLSTM with self-attention: The ultimate trio for customer sentiment analysis—0
Ontology of Belief Diversity: A Community-Based Epistemological Approach—0
Sentiment Reasoning for HealthcareCode3
Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis—0
Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach—0
ZZU-NLP at SIGHAN-2024 dimABSA Task: Aspect-Based Sentiment Analysis with Coarse-to-Fine In-context Learning—0
Economy Watchers Survey Provides Datasets and Tasks for Japanese Financial DomainCode0
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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