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 651–700 of 5630 papers

TitleStatusHype
AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection—0
Affect Proxies and Ontological Change: A finance case study—0
A Convolutional Neural Network for Aspect Sentiment Classification—0
An Eye-tracking Study of Named Entity Annotation—0
Affective Common Sense Knowledge Acquisition for Sentiment Analysis—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
Affection Driven Neural Networks for Sentiment Analysis—0
A Case Study of Machine Translation in Financial Sentiment Analysis—0
Automatic Construction of an Annotated Corpus with Implicit Aspects—0
Automatic evaluation of scientific abstracts through natural language processing—0
An Exploration of Discourse-Based Sentence Spaces for Compositional Distributional Semantics—0
An Experiment in Integrating Sentiment Features for Tech Stock Prediction in Twitter—0
Affect in Tweets Using Experts Model—0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets—0
A New View of Multi-modal Language Analysis: Audio and Video Features as Text ``Styles''—0
A Case Study of Chinese Sentiment Analysis on Social Media Reviews Based on LSTM—0
Automatically Constructing a Normalisation Dictionary for Microblogs—0
A New Statistical Approach for Comparing Algorithms for Lexicon Based Sentiment Analysis—0
A New Approach To Text Rating Classification Using Sentiment Analysis—0
Aff2Vec: Affect--Enriched Distributional Word Representations—0
An Evaluation of the Brazilian Portuguese LIWC Dictionary for Sentiment Analysis—0
A context-based model for Sentiment Analysis in Twitter—0
Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis—0
Automatically Inferring Implicit Properties in Similes—0
A Context-based Disambiguation Model for Sentiment Concepts Using a Bag-of-concepts Approach—0
An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses—0
A Concrete Chinese NLP Pipeline—0
An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing—0
A Neural Network Model for Low-Resource Universal Dependency Parsing—0
Aesthetic Visual Question Answering of Photographs—0
Automatically augmenting an emotion dataset improves classification using audio—0
A Neural Network for Factoid Question Answering over Paragraphs—0
Adverse Media Mining for KYC and ESG Compliance—0
An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews—0
An Ensemble Model for Sentiment Analysis of Hindi-English Code-Mixed Data—0
Automatically Annotating A Five-Billion-Word Corpus of Japanese Blogs for Affect and Sentiment Analysis—0
Automatically Building a Corpus for Sentiment Analysis on Indonesian Tweets—0
Automatically Labeling $200B Life-Saving Datasets: A Large Clinical Trial Outcome Benchmark—0
Automatic Extraction of Agriculture Terms from Domain Text: A Survey of Tools and Techniques—0
An Ensemble Method with Sentiment Features and Clustering Support—0
An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM—0
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives—0
An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies—0
An end-to-end Neural Network Framework for Text Clustering—0
A Conceptual Framework for Inferring Implicatures—0
An End-To-End LLM Enhanced Trading System—0
An End-to-End Homomorphically Encrypted Neural Network—0
A Computational Approach to Walt Whitman's Stylistic Changes in Leaves of Grass—0
An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding—0
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis—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