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Open AccessProceedings ArticleDOI

Multitask Learning for Fine-Grained Twitter Sentiment Analysis

TLDR
This study demonstrates the potential of multitask models on this type of problems and improves the state-of-the-art results in the fine-grained sentiment classification problem.
Abstract
Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately. We argue that such classification tasks are correlated and we propose a multitask approach based on a recurrent neural network that benefits by jointly learning them. Our study demonstrates the potential of multitask models on this type of problems and improves the state-of-the-art results in the fine-grained sentiment classification problem.

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Citations
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Proceedings ArticleDOI

Transfer Learning in Natural Language Processing.

TL;DR: Transfer learning as discussed by the authors is a set of methods that extend the classical supervised machine learning paradigm by leveraging data from additional domains or tasks to train a model with better generalization properties, which can be used for NLP tasks.
Proceedings ArticleDOI

Disentangled Representation Learning for Non-Parallel Text Style Transfer

TL;DR: A simple yet effective approach is proposed, which incorporates auxiliary multi-task and adversarial objectives, for style prediction and bag-of-words prediction, respectively, and this disentangled latent representation learning can be applied to style transfer on non-parallel corpora.
Journal ArticleDOI

All-in-One: Emotion, Sentiment and Intensity Prediction using a Multi-task Ensemble Framework

TL;DR: A multi-task ensemble framework that jointly learns multiple related problems of emotion and sentiment analysis and outperforms the single-task frameworks in all experiments.
Proceedings ArticleDOI

Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training

TL;DR: Emo2Vec is proposed which encodes emotional semantics into vectors and outperforms existing affect-related representations, such as Sentiment-Specific Word Embedding and DeepMoji embeddings with much smaller training corpora.
References
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Journal ArticleDOI

Long short-term memory

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal Article

Scikit-learn: Machine Learning in Python

TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Proceedings ArticleDOI

Glove: Global Vectors for Word Representation

TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Proceedings Article

Understanding the difficulty of training deep feedforward neural networks

TL;DR: The objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future.
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