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 2801–2850 of 5630 papers

TitleStatusHype
We Usually Don't Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter—0
What's up on Twitter? Catch up with TWIST!—0
What BERTs and GPTs know about your brand? Probing contextual language models for affect associations—0
What confuses BERT? Linguistic Evaluation of Sentiment Analysis on Telecom Customer Opinion—0
What Does a TextCNN Learn?—0
What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis—0
What Emotions Make One or Five Stars? Understanding Ratings of Online Product Reviews by Sentiment Analysis and XAI—0
What is Sentiment Meant to Mean to Language Models?—0
``What Is Your Evidence?'' A Study of Controversial Topics on Social Media—0
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations—0
What Sentiment and Fun Facts We Learnt Before FIFA World Cup Qatar 2022 Using Twitter and AI—0
What Sentiments Can Be Found in Medical Forums?—0
What we really want to find by Sentiment Analysis: The Relationship between Computational Models and Psychological State—0
What we write about when we write about causality: Features of causal statements across large-scale social discourse—0
What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning—0
Wheel of Life: an initial investigation. Topic-Related Polarity Visualization in Personal Stories—0
When and Why a Model Fails? A Human-in-the-loop Error Detection Framework for Sentiment Analysis—0
When and Why does a Model Fail? A Human-in-the-loop Error Detection Framework for Sentiment Analysis—0
When Are Tree Structures Necessary for Deep Learning of Representations?—0
When Crowd Meets Persona: Creating a Large-Scale Open-Domain Persona Dialogue Corpus—0
When does CLIP generalize better than unimodal models? When judging human-centric concepts—0
When does deep multi-task learning work for loosely related document classification tasks?—0
When More is not Necessary Better: Multilingual Auxiliary Tasks for Zero-Shot Cross-Lingual Transfer of Hate Speech Detection Models—0
When Saliency Meets Sentiment: Understanding How Image Content Invokes Emotion and Sentiment—0
When Word Embeddings Become Endangered—0
Where does active travel fit within local community narratives of mobility space and place?—0
Which is Making the Contribution: Modulating Unimodal and Cross-modal Dynamics for Multimodal Sentiment Analysis—0
Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.—0
Who Did What to Whom? A Contrastive Study of Syntacto-Semantic Dependencies—0
`Who would have thought of that!': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection—0
Why is "Problems" Predictive of Positive Sentiment? A Case Study of Explaining Unintuitive Features in Sentiment Classification—0
Why Question Answering using Sentiment Analysis and Word Classes—0
Why Words Alone Are Not Enough: Error Analysis of Lexicon-based Polarity Classifier for Czech—0
WiDe-analysis: Enabling One-click Content Moderation Analysis on Wikipedia's Articles for Deletion—0
Wikipedia Titles As Noun Tag Predictors—0
Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT—0
Will\_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy—0
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining—0
WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge—0
WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony—0
Word2rate: training and evaluating multiple word embeddings as statistical transitions—0
Word2Vec and Doc2Vec in Unsupervised Sentiment Analysis of Clinical Discharge Summaries—0
Word Affect Intensities—0
Word Embedding Algorithms as Generalized Low Rank Models and their Canonical Form—0
Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums—0
Word Embedding and WordNet Based Metaphor Identification and Interpretation—0
Word Embedding-based Antonym Detection using Thesauri and Distributional Information—0
Word Embedding Evaluation for Sinhala—0
Word Embeddings for Banking Industry—0
Word Embeddings for Code-Mixed Language Processing—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