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

Showing 27512800 of 5630 papers

TitleStatusHype
TextDecepter: Hard Label Black Box Attack on Text ClassifiersCode0
Feature Extraction Functions for Neural Logic Rule Learning0
Modeling Inter-Aspect Dependencies with a Non-temporal Mechanism for Aspect-Based Sentiment Analysis0
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word EmbeddingsCode0
On Commonsense Cues in BERT for Solving Commonsense Tasks0
SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets0
C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering0
A Context-based Disambiguation Model for Sentiment Concepts Using a Bag-of-concepts Approach0
Using LDA and LSTM Models to Study Public Opinions and Critical Groups Towards Congestion Pricing in New York City through 2007 to 20190
TPFN: Applying Outer Product along Time to Multimodal Sentiment Analysis Fusion on Incomplete Data0
Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology using Natural Language Processing and Deep Learning for Smarter Decision Aid. Case study of restaurant choice using TripAdvisor reviewsCode0
A Study of fastText Word Embedding Effects in Document Classification in Bangla LanguageCode0
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion AnalysisCode0
Deep Learning Brasil -- NLP at SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets0
Improving Results on Russian Sentiment DatasetsCode0
Preparation of Sentiment tagged Parallel Corpus and Testing its effect on Machine Translation0
ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text0
Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis0
Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding0
JUNLP@SemEval-2020 Task 9:Sentiment Analysis of Hindi-English code mixed data using Grid Search Cross Validation0
IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text using Deep Neural Networks and Linear Baselines0
FiSSA at SemEval-2020 Task 9: Fine-tuned For FeelingsCode0
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News0
HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed TextsCode0
NITS-Hinglish-SentiMix at SemEval-2020 Task 9: Sentiment Analysis For Code-Mixed Social Media Text Using an Ensemble Model0
Inferring Political Preferences from Twitter0
Morphological Skip-Gram: Using morphological knowledge to improve word representation0
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language TasksCode0
A novel approach to sentiment analysis in Persian using discourse and external semantic information0
Feature-level Rating System using Customer Reviews and Review Votes0
A Framework for Capturing and Analyzing Unstructured and Semi-structured Data for a Knowledge Management System0
What Can We Learn From Almost a Decade of Food TweetsCode0
Cautious Monotonicity in Case-Based Reasoning with Abstract Argumentation0
Automatic Detection of Sexist Statements Commonly Used at the WorkplaceCode0
The impact of political party/candidate on the election results from a sentiment analysis perspective using #AnambraDecides2017 tweets0
Research on Annotation Rules and Recognition Algorithm Based on Phrase Window0
EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble MethodsCode0
News Sentiment Analysis0
Sentiment Analysis on Customer Responses0
Tweets Sentiment Analysis via Word Embeddings and Machine Learning Techniques0
Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes0
Sentiment Analysis on Social Media Content0
Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey0
A Novel BGCapsule Network for Text Classification0
Sequential Domain Adaptation through Elastic Weight Consolidation for Sentiment Analysis0
Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection0
Towards Reversal-Based Textual Data Augmentation for NLI Problems with Opposable Classes0
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples0
e-Commerce and Sentiment Analysis: Predicting Outcomes of Class Action Lawsuits0
SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100Unverified
2T5-11BAccuracy97.5Unverified
3MT-DNN-SMARTAccuracy97.5Unverified
4T5-3BAccuracy97.4Unverified
5MUPPET Roberta LargeAccuracy97.4Unverified
6StructBERTRoBERTa ensembleAccuracy97.1Unverified
7ALBERTAccuracy97.1Unverified
8XLNet (single model)Accuracy97Unverified
9SMARTRoBERTaDev Accuracy96.9Unverified
10ELECTRAAccuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large with LlamBERTAccuracy96.68Unverified
2RoBERTa-largeAccuracy96.54Unverified
3XLNetAccuracy96.21Unverified
4Heinsen Routing + RoBERTa LargeAccuracy96.2Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.1Unverified
6GraphStarAccuracy96Unverified
7DV-ngrams-cosine with NB sub-sampling + RoBERTa.baseAccuracy95.94Unverified
8DV-ngrams-cosine + RoBERTa.baseAccuracy95.92Unverified
9Roberta_Large ST + Cosine Similarity LossAccuracy95.9Unverified
10BERT large finetune UDAAccuracy95.8Unverified
#ModelMetricClaimedVerifiedStatus
1Llama-3.3-70B + CAPOAccuracy62.27Unverified
2Mistral-Small-24B + CAPOAccuracy 60.2Unverified
3Heinsen Routing + RoBERTa LargeAccuracy59.8Unverified
4RoBERTa-large+Self-ExplainingAccuracy59.1Unverified
5Qwen2.5-32B + CAPOAccuracy 59.07Unverified
6Heinsen Routing + GPT-2Accuracy58.5Unverified
7BCN+Suffix BiLSTM-Tied+CoVeAccuracy56.2Unverified
8BERT LargeAccuracy55.5Unverified
9LM-CPPF RoBERTa-baseAccuracy54.9Unverified
10BCN+ELMoAccuracy54.7Unverified
#ModelMetricClaimedVerifiedStatus
1Char-level CNNError4.88Unverified
2SVDCNNError4.74Unverified
3LEAMError4.69Unverified
4fastText, h=10, bigramError4.3Unverified
5SWEM-hierError4.19Unverified
6SRNNError3.96Unverified
7M-ACNNError3.89Unverified
8DNC+CUWError3.6Unverified
9CCCapsNetError3.52Unverified
10Block-sparse LSTMError3.27Unverified
#ModelMetricClaimedVerifiedStatus
1Millions of EmojiTraining Time1,500Unverified
2VLAWEAccuracy93.3Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy92.5Unverified
4AnglE-LLaMA-7BAccuracy91.09Unverified
5byte mLSTM7Accuracy86.8Unverified
6MEANAccuracy84.5Unverified
7RNN-CapsuleAccuracy83.8Unverified
8Capsule-BAccuracy82.3Unverified
9SuBiLSTM-TiedAccuracy81.6Unverified
10USE_T+CNNAccuracy81.59Unverified