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 4451–4500 of 5630 papers

TitleStatusHype
An End-to-End Homomorphically Encrypted Neural Network—0
An End-To-End LLM Enhanced Trading System—0
An end-to-end Neural Network Framework for Text Clustering—0
An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies—0
An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM—0
An Ensemble Method with Sentiment Features and Clustering Support—0
An Ensemble Model for Sentiment Analysis of Hindi-English Code-Mixed Data—0
An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews—0
A Neural Network for Factoid Question Answering over Paragraphs—0
A Neural Network Model for Low-Resource Universal Dependency Parsing—0
An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing—0
An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses—0
An Evaluation of the Brazilian Portuguese LIWC Dictionary for Sentiment Analysis—0
A New Approach To Text Rating Classification Using Sentiment Analysis—0
A New Statistical Approach for Comparing Algorithms for Lexicon Based Sentiment Analysis—0
A New View of Multi-modal Language Analysis: Audio and Video Features as Text ``Styles''—0
An Experiment in Integrating Sentiment Features for Tech Stock Prediction in Twitter—0
An Exploration of Discourse-Based Sentence Spaces for Compositional Distributional Semantics—0
An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection—0
An Eye-tracking Study of Named Entity Annotation—0
AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection—0
An Hymn of an even Deeper Sentiment Analysis—0
An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management—0
An Improved Reinforcement Learning Model Based on Sentiment Analysis—0
An Improved Text Sentiment Classification Model Using TF-IDF and Next Word Negation—0
An Indian Language Social Media Collection for Hate and Offensive Speech—0
An Integrated NPL Approach to Sentiment Analysis in Satisfaction Surveys—0
An Intelligent Data Analysis for Hotel Recommendation Systems using Machine Learning—0
An Introductory Survey on Attention Mechanisms in NLP Problems—0
An Investigation for Implicatures in Chinese : Implicatures in Chinese and in English are similar !—0
An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese—0
An Iterative Algorithm for Rescaled Hyperbolic Functions Regression—0
An LSTM Approach to Short Text Sentiment Classification with Word Embeddings—0
An LSTM model for Twitter Sentiment Analysis—0
Annotated Corpus for Sentiment Analysis in Odia Language—0
Annotating Italian Social Media Texts in Universal Dependencies—0
Annotating Modal Expressions in the Chinese Treebank—0
Annotating Opinions and Opinion Targets in Student Course Feedback—0
Annotating Opinions in German Political News—0
Annotating Sentiment and Irony in the Online Italian Political Debate on \#labuonascuola—0
Annotating the Interaction between Focus and Modality: the case of exclusive particles—0
Annotating Uncertainty in Hungarian Webtext—0
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus—0
Annotation Scheme for Constructing Sentiment Corpus in Korean—0
An opinion about opinions about opinions: subjectivity and the aggregate reader—0
Anotando um Corpus de Not\' para a An\'alise de Sentimentos: um Relato de Experi\^encia (Annotating a corpus of News for Sentiment Analysis: An Experience Report)—0
A novel approach to sentiment analysis in Persian using discourse and external semantic information—0
A novel Bayesian estimation-based word embedding model for sentiment analysis—0
A Novel BGCapsule Network for Text Classification—0
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC—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