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 2551–2600 of 5630 papers

TitleStatusHype
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
A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition—0
Convolutional neural network compression for natural language processing—0
Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction—0
Conversational Recommendation System using NLP and Sentiment Analysis—0
A Multimodal Framework for Topic Propagation Classification in Social Networks—0
Controlled CNN-based Sequence Labeling for Aspect Extraction—0
Aspect category learning and sentimental analysis using weakly supervised learning—0
Contrastive Clustering: Toward Unsupervised Bias Reduction for Emotion and Sentiment Classification—0
Contrasting the efficiency of stock price prediction models using various types of LSTM models aided with sentiment analysis—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
Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network—0
IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection—0
Contextual Text Embeddings for Twi—0
A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining—0
Contextual Sentence Analysis for the Sentiment Prediction on Financial Data—0
IIP at SemEval-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of Tweets—0
Contextual Recurrent Units for Cloze-style Reading Comprehension—0
IIITG-ADBU at SemEval-2020 Task 9: SVM for Sentiment Analysis of English-Hindi Code-Mixed Text—0
IHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence—0
Contextual explanation rules for neural clinical classifiers—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
If you've got it, flaunt it: Making the most of fine-grained sentiment annotations—0
IFoodCloud: A Platform for Real-time Sentiment Analysis of Public Opinion about Food Safety in China—0
idT5: Indonesian Version of Multilingual T5 Transformer—0
Idioms-Proverbs Lexicon for Modern Standard Arabic and Colloquial Sentiment Analysis—0
Idiom Detection in Sorani Kurdish Texts—0
Idiom-Aware Compositional Distributed Semantics—0
IDI@NTNU at SemEval-2016 Task 6: Detecting Stance in Tweets Using Shallow Features and GloVe Vectors for Word Representation—0
Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis—0
Aspect-based Sentiment Classification via Reinforcement Learning—0
Ideological Perspective Detection Using Semantic Features—0
Identifying Where to Focus in Reading Comprehension for Neural Question Generation—0
Contextual Augmented Global Contrast for Multimodal Intent Recognition—0
Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification—0
``i have a feeling trump will win..................'': Forecasting Winners and Losers from User Predictions on Twitter—0
Identifying the sentiment styles of YouTube's vloggers—0
IHS R\&D Belarus: Cross-domain extraction of product features using CRF—0
Contextual and Position-Aware Factorization Machines for Sentiment Classification—0
IIIT-H at SemEval 2015: Twitter Sentiment Analysis -- The Good, the Bad and the Neutral!—0
Identifying Sentiment Words Using an Optimization-based Model without Seed Words—0
Identifying Sentiments in Algerian Code-switched User-generated Comments—0
IITB-Sentiment-Analysts: Participation in Sentiment Analysis in Twitter SemEval 2013 Task—0
IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction—0
Identifying Restaurant Features via Sentiment Analysis on Yelp Reviews—0
Aspect Based Sentiment Analysis with Self-Attention and Gated Convolutional Networks—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