Sentiment Classification
305 papers with code • 1 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Sentiment Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|
Libraries
Use these libraries to find Sentiment Classification models and implementationsDatasets
SST
Yelp
StockEmotions
Most implemented papers
A Structured Self-attentive Sentence Embedding
jadore801120/attention-is-all-you-need-pytorch
•
•
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
stanfordnlp/treelstm
•
•
IJCNLP 2015
Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks.
A C-LSTM Neural Network for Text Classification
In this work, we combine the strengths of both architectures and propose a novel and unified model called C-LSTM for sentence representation and text classification.
Effective LSTMs for Target-Dependent Sentiment Classification
songyouwei/ABSA-PyTorch
•
•
COLING 2016
Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence.
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
Aspect Level Sentiment Classification with Deep Memory Network
songyouwei/ABSA-PyTorch
•
•
EMNLP 2016
Such importance degree and text representation are calculated with multiple computational layers, each of which is a neural attention model over an external memory.
Quasi-Recurrent Neural Networks
Recurrent neural networks are a powerful tool for modeling sequential data, but the dependence of each timestep's computation on the previous timestep's output limits parallelism and makes RNNs unwieldy for very long sequences.
Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders
andi611/Self-Supervised-Speech-Pretraining-and-Representation-Learning
•
•
We present Mockingjay as a new speech representation learning approach, where bidirectional Transformer encoders are pre-trained on a large amount of unlabeled speech.
Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis
In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE).
