Sentiment Classification

264 papers with code • 1 benchmarks • 2 datasets

This task has no description! Would you like to contribute one?


Use these libraries to find Sentiment Classification models and implementations

Most implemented papers

A Structured Self-attentive Sentence Embedding

jadore801120/attention-is-all-you-need-pytorch 9 Mar 2017

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

zackhy/TextClassification 27 Nov 2015

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.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding

PaddlePaddle/PaddleNLP 31 Aug 2019

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.

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.

Quasi-Recurrent Neural Networks

salesforce/pytorch-qrnn 5 Nov 2016

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.

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.

Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders

andi611/Self-Supervised-Speech-Pretraining-and-Representation-Learning 25 Oct 2019

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

xuuuluuu/SemEval-Triplet-data 5 Nov 2019

In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE).