Sentiment Analysis
Sentiment analysis is the task of classifying the polarity of a given text.
State-of-the-art leaderboards
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Subtasks
Greatest papers with code
Adversarial Training Methods for Semi-Supervised Text Classification
Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting. However, both methods require making small perturbations to numerous entries of the input vector, which is inappropriate for sparse high-dimensional inputs such as one-hot word representations.
#3 best model for
Sentiment Analysis on IMDb
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data. This paper studies CNN on text categorization to exploit the 1D structure (namely, word order) of text data for accurate prediction.
#5 best model for
Sentiment Analysis on IMDb
Deep contextualized word representations
HLT 2018
• zalandoresearch/flair
• 
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus.
#2 best model for
Coreference Resolution on CoNLL 2012
COREFERENCE RESOLUTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS
Convolutional Neural Networks for Sentence Classification
EMNLP 2014
• dennybritz/cnn-text-classification-tf
• 
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks.
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. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.
NATURAL LANGUAGE INFERENCE SENTENCE EMBEDDING SENTIMENT ANALYSIS
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
IJCNLP 2015
• tensorflow/fold
•
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. The only underlying LSTM structure that has been explored so far is a linear chain.
The Natural Language Decathlon: Multitask Learning as Question Answering
ICLR 2019
• salesforce/decaNLP
•
Furthermore, we present a new Multitask Question Answering Network (MQAN) jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
DOMAIN ADAPTATION MACHINE TRANSLATION NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING RELATION EXTRACTION SEMANTIC PARSING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING
Universal Sentence Encoder
• facebookresearch/InferSent
•
For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance. We find that transfer learning using sentence embeddings tends to outperform word level transfer.
SOTA for Text Classification on TREC-6
SEMANTIC TEXTUAL SIMILARITY SENTENCE EMBEDDINGS SENTIMENT ANALYSIS SUBJECTIVITY ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING WORD EMBEDDINGS
Learning to Generate Reviews and Discovering Sentiment
ICLR 2018
• openai/generating-reviews-discovering-sentiment
•
We explore the properties of byte-level recurrent language models. When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts.
#3 best model for
Sentiment Analysis on SST-2 Binary classification
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. We introduce quasi-recurrent neural networks (QRNNs), an approach to neural sequence modeling that alternates convolutional layers, which apply in parallel across timesteps, and a minimalist recurrent pooling function that applies in parallel across channels.
#16 best model for
Machine Translation on IWSLT2015 German-English