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
A Unified Framework for Structured Prediction: From Theory to Practice—0
Natural Language Processing in Political Campaigns—0
Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction—0
Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization—0
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification—0
Towards an integrated pipeline for aspect-based sentiment analysis in various domains—0
Mining Argumentative Structure from Natural Language text using Automatically Generated Premise-Conclusion Topic Models—0
Evaluating the morphological compositionality of polarityCode0
Detecting Sarcasm Using Different Forms Of Incongruity—0
Towards a Universal Sentiment Classifier in Multiple languages—0
A Case Study of Machine Translation in Financial Sentiment Analysis—0
Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia—0
Using lexical level information in discourse structures for Basque sentiment analysis—0
Did you ever read about Frogs drinking Coffee? Investigating the Compositionality of Multi-Emoji Expressions—0
Assessing Objective Recommendation Quality through Political Forecasting—0
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning—0
A Calibration Method for Evaluation of Sentiment Analysis—0
Initializing Convolutional Filters with Semantic Features for Text Classification—0
Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective—0
``Haters gonna hate'': challenges for sentiment analysis of Facebook comments in Brazilian Portuguese—0
Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach—0
Investigating Redundancy in Emoji Use: Study on a Twitter Based Corpus—0
Translating Dialectal Arabic as Low Resource Language using Word Embedding—0
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC—0
Learning Contextually Informed Representations for Linear-Time Discourse Parsing—0
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus—0
Recurrent Attention Network on Memory for Aspect Sentiment AnalysisCode0
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components—0
Annotating Italian Social Media Texts in Universal Dependencies—0
An Eye-tracking Study of Named Entity Annotation—0
Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension—0
Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees—0
Large-scale news entity sentiment analysis—0
Unsupervised Aspect Term Extraction with B-LSTM \& CRF using Automatically Labelled Datasets—0
Comparing Attitudes to Climate Change in the Media using sentiment analysis based on Latent Dirichlet Allocation—0
Gender Prediction for Chinese Social Media Data—0
Analysing Market Sentiments: Utilising Deep Learning to Exploit Relationships within the Economy—0
Word Embeddings for Multi-label Document Classification—0
Good News vs. Bad News: What are they talking about?—0
Computational Sarcasm—0
LIPN-UAM at EmoInt-2017:Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination—0
GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection—0
Graph-based Event Extraction from Twitter—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
Sentiment Analysis by Joint Learning of Word Embeddings and Classifier—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