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 2851–2900 of 5630 papers

TitleStatusHype
廣義知網詞彙意見極性的預測 (Predicting the Semantic Orientation of Terms in E-HowNet) [In Chinese]—0
Predicting Treatment Outcome from Patient Texts:The Case of Internet-Based Cognitive Behavioural Therapy—0
Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities—0
Predicting Valence-Arousal Ratings of Words Using a Weighted Graph Method—0
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models—0
Predict the Future from the Past? On the Temporal Data Distribution Shift in Financial Sentiment Classifications—0
Pre-Finetuning with Impact Duration Awareness for Stock Movement Prediction—0
Preliminary Steps Towards Federated Sentiment Classification—0
Preparation of Improved Turkish DataSet for Sentiment Analysis in Social Media—0
Preparation of Sentiment tagged Parallel Corpus and Testing its effect on Machine Translation—0
Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of Indian Languages—0
Presence of informal language, such as emoticons, hashtags, and slang, impact the performance of sentiment analysis models on social media text?—0
weighted CapsuleNet networks for Persian multi-domain sentiment analysis—0
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic—0
Pre-training Pre-trained Models with Auxiliary Labels and Fine-tuning for Text Classification—0
Pretraining Sentiment Classifiers with Unlabeled Dialog Data—0
PRFashion24: A Dataset for Sentiment Analysis of Fashion Products Reviews in Persian—0
PRHLT: Combination of Deep Autoencoders with Classification and Regression Techniques for SemEval-2015 Task 11—0
PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis—0
Prior Polarity Lexical Resources for the Italian Language—0
Prior versus Contextual Emotion of a Word in a Sentence—0
Privacy-Aware Crowd Labelling for Machine Learning Tasks—0
Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning—0
Private Transformer Inference in MLaaS: A Survey—0
PrivySense: Price Volatility based Sentiments Estimation from Financial News using Machine Learning—0
Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions—0
Problematic Cases in the Annotation of Negation in Spanish—0
Proceedings of the 2nd Workshop on Sentiment Analysis where AI meets Psychology—0
Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis—0
Proceedings of the 3rd Workshop on Sentiment Analysis where AI meets Psychology—0
Product Feature Mining: Semantic Clues versus Syntactic Constituents—0
Product Market Demand Analysis Using NLP in Banglish Text with Sentiment Analysis and Named Entity Recognition—0
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models—0
PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts—0
Prompt Engineering Using GPT for Word-Level Code-Mixed Language Identification in Low-Resource Dravidian Languages—0
PromptExp: Multi-granularity Prompt Explanation of Large Language Models—0
Prompt-Learning for Fine-Grained Entity Typing—0
Prompt-Learning for Fine-Grained Entity Typing—0
Prompt Sentiment: The Catalyst for LLM Change—0
Propagation de polarit\'es dans des familles de mots : impact de la morphologie dans la construction d'un lexique pour l'analyse de sentiments (Spreading Polarities among Word Families: Impact of Morphology on Building a Lexicon for Sentiment Analysis) [in French]—0
Prune Once for All: Sparse Pre-Trained Language Models—0
Pseudo-Label Guided Unsupervised Domain Adaptation of Contextual Embeddings—0
Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method—0
Psychological State in Text: A Limitation of Sentiment Analysis—0
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis—0
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment—0
Public Apologies in India - Semantics, Sentiment and Emotion—0
Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence—0
Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media—0
Public sentiments on the fourth industrial revolution: An unsolicited public opinion poll from Twitter—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