We propose and test multiple neuro-symbolic methods for sentiment analysis. They combine
deep neural networks–transformers and recurrent neural networks–with external knowledge …

We introduce a tree-structured attention neural network for sentences and small phrases
and apply it to the problem of sentiment classification. Our model expands the current …

We formalize the notion of a pseudo-ensemble, a (possibly infinite) collection of child
models spawned from a parent model by perturbing it according to some noise process. Eg …

This paper addresses the problem of sentence-level sentiment analysis. In recent years,
Convolution and Recursive Neural Networks have been proven to be effective network …

Recursive neural models have achieved promising results in many natural language
processing tasks. The main difference among these models lies in the composition function …

Traditional approaches to sentiment classification rely on lexical features, syntax-based
features or a combination of the two. We propose semantic features using word senses for a …

In recent years, with the development of social networks, sentiment analysis has become
one of the most important research topics in the field of natural language processing. The …

There has been a good amount of progress in sentiment analysis over the past 10 years,
including the proposal of new methods and the creation of benchmark datasets. In some …

Sentiment lexicons have been leveraged as a useful source of features for sentiment
analysis models, leading to the state-of-the-art accuracies. On the other hand, most existing …

SenticNet is a publicly available semantic and affective resource for concept-level sentiment
analysis. Rather than using graph-mining and dimensionality-reduction techniques …