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 1151–1200 of 5630 papers

TitleStatusHype
Aspect-Level Cross-lingual Sentiment Classification with Constrained SMT—0
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis—0
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification—0
A Dependency Parser for Tweets—0
Aspect Extraction with Automated Prior Knowledge Learning—0
Aspect Extraction Using Coreference Resolution and Unsupervised Filtering—0
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations—0
Aspect Extraction through Semi-Supervised Modeling—0
Aspect Extraction and Sentiment Classification of Mobile Apps using App-Store Reviews—0
A Multimodal Sentiment Dataset for Video Recommendation—0
A Comparative Study of Sentiment Analysis Using NLP and Different Machine Learning Techniques on US Airline Twitter Data—0
Contrasting the efficiency of stock price prediction models using various types of LSTM models aided with sentiment analysis—0
Controlled CNN-based Sequence Labeling for Aspect Extraction—0
A Multimodal Framework for Topic Propagation Classification in Social Networks—0
A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts—0
Aspect category learning and sentimental analysis using weakly supervised learning—0
Aspect Category Detection via Topic-Attention Network—0
A Multilingual Sentiment Lexicon for Low-Resource Language Translation using Large Languages Models and Explainable AI—0
Contextual Sentence Analysis for the Sentiment Prediction on Financial Data—0
Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network—0
A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining—0
Examining Structure of Word Embeddings with PCA—0
Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): an NLP-based Method for Evaluating Chess Strategies from Textbooks—0
A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis—0
A deep Natural Language Inference predictor without language-specific training data—0
Contextual Recurrent Units for Cloze-style Reading Comprehension—0
Contextual Text Embeddings for Twi—0
Aspect-based Sentiment Classification via Reinforcement Learning—0
A Deep Learning System for Sentiment Analysis of Service Calls—0
Aspect Based Sentiment Analysis with Self-Attention and Gated Convolutional Networks—0
A Multidisciplinary Approach to Telegram Data Analysis—0
Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis—0
Aspect-based Sentiment Analysis with Opinion Tree Generation—0
A Multi-Dimensional Bayesian Approach to Lexical Style—0
A multiclass Q-NLP sentiment analysis experiment using DisCoCat—0
Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations—0
Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa—0
A deep-learning framework to detect sarcasm targets—0
Contextual explanation rules for neural clinical classifiers—0
Conversational Recommendation System using NLP and Sentiment Analysis—0
#Coronavirus or #Chinesevirus?!: Understanding the negative sentiment reflected in Tweets with racist hashtags across the development of COVID-19—0
Aspect-Based Sentiment Analysis Using Bitmask Bidirectional Long Short Term Memory Networks—0
Aspect-Based Sentiment Analysis using BERT—0
A Multi-Agent Probabilistic Inference Framework Inspired by Kairanban-Style CoT System with IdoBata Conversation for Debiasing—0
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture—0
Amrita\_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets—0
A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot—0
Aspect Based Sentiment Analysis Using Spectral Temporal Graph Neural Network—0
Aspect Based Sentiment Analysis using Sentiment Flow with Local and Non-local Neighbor Information—0
Amplifying Aspect-Sentence Awareness: A Novel Approach for Aspect-Based Sentiment Analysis—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