Sentiment Classification
306 papers with code • 0 benchmarks • 8 datasets
Benchmarks
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Libraries
Use these libraries to find Sentiment Classification models and implementationsDatasets
Yelp
StockEmotions
Most implemented papers
Interactive Attention Networks for Aspect-Level Sentiment Classification
In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning.
Attentional Encoder Network for Targeted Sentiment Classification
Most of the previous approaches model context and target words with RNN and attention.
Induction Networks for Few-Shot Text Classification
zhongyuchen/few-shot-text-classification
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IJCNLP 2019
Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries.
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
pvgladkov/knowledge-distillation
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In the natural language processing literature, neural networks are becoming increasingly deeper and complex.
Confident Learning: Estimating Uncertainty in Dataset Labels
Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence.
Adversarial Training for Aspect-Based Sentiment Analysis with BERT
IMPLabUniPr/Adversarial-Training-for-ABSA
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In this work, we apply adversarial training, which was put forward by Goodfellow et al. (2014), to the post-trained BERT (BERT-PT) language model proposed by Xu et al. (2019) on the two major tasks of Aspect Extraction and Aspect Sentiment Classification in sentiment analysis.
Fast and accurate sentiment classification using an enhanced Naive Bayes model
We have explored different methods of improving the accuracy of a Naive Bayes classifier for sentiment analysis.
Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification
A well-known approach for aggregation is the Dawid-Skene (DS) algorithm, which is based on the principle of Expectation-Maximization (EM).
Cross-Lingual Sentiment Quantification
Cross-lingual sentiment quantification (and cross-lingual \emph{text} quantification in general) has never been discussed before in the literature; we establish baseline results for the binary case by combining state-of-the-art quantification methods with methods capable of generating cross-lingual vectorial representations of the source and target documents involved.
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification
deepopinion/domain-adapted-atsc
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LREC 2020
Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect-Based Sentiment Analysis (ABSA), which has many applications e. g. in e-commerce, where data and insights from reviews can be leveraged to create value for businesses and customers.
