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 4051–4100 of 5630 papers

TitleStatusHype
Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems—0
Idiom-Aware Compositional Distributed Semantics—0
Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings—0
A Cognition Based Attention Model for Sentiment Analysis—0
Opinion Recommendation Using A Neural Model—0
Learning Contextually Informed Representations for Linear-Time Discourse Parsing—0
Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network—0
A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis—0
Sentiment Lexicon Construction with Representation Learning Based on Hierarchical Sentiment SupervisionCode0
Initializing Convolutional Filters with Semantic Features for Text Classification—0
Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association—0
Assessing Objective Recommendation Quality through Political Forecasting—0
Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia—0
Towards a Universal Sentiment Classifier in Multiple languages—0
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC—0
Identifying Where to Focus in Reading Comprehension for Neural Question Generation—0
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components—0
Human Centered NLP with User-Factor Adaptation—0
Refining Word Embeddings for Sentiment Analysis—0
Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction—0
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification—0
Recurrent Attention Network on Memory for Aspect Sentiment AnalysisCode0
Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension—0
``i have a feeling trump will win..................'': Forecasting Winners and Losers from User Predictions on Twitter—0
AutoExtend: Combining Word Embeddings with Semantic Resources—0
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models—0
Impact of Feature Selection on Micro-Text Classification—0
Robust Task Clustering for Deep Many-Task Learning—0
Explainable Recommendation: Theory and Applications—0
Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVMCode0
Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker ContainerCode0
Data Sets: Word Embeddings Learned from Tweets and General Data—0
Sentiment Analysis by Joint Learning of Word Embeddings and Classifier—0
Leveraging Sparse and Dense Feature Combinations for Sentiment Classification—0
Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese—0
Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNNCode0
Using Convolutional Neural Networks to Classify Hate-Speech—0
Exploring Joint Neural Model for Sentence Level Discourse Parsing and Sentiment Analysis—0
Gradual Learning of Matrix-Space Models of Language for Sentiment Analysis—0
On the ``Calligraphy'' of Books—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
Personality Driven Differences in Paraphrase Preference—0
Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits—0
Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information—0
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring—0
Feature Selection as Causal Inference: Experiments with Text Classification—0
Learning Word Representations with Regularization from Prior Knowledge—0
TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification—0
MI\&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification—0
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment AnalysisCode0
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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