Stanford Sentiment Treebank

Sentiment analysis of short texts such as single sentences and Twitter messages is challenging because of the limited contextual information that they normally contain. Effectively solving this task requires strategies …...

This paper proposes neural networks for inte- grating compositional and non-compositional sentiment in the process of sentiment compo- sition, a type of semantic composition that op- timizes a sentiment objective. …...

In this paper, we propose a baseline message- level sentiment classification method, as de- veloped for SemEval-2015 Task 10, Subtask B. This system leverages both hand-crafted features and message-level embedding …...

AbstractRecurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variable-length text. RNN …...

AbstractWord embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks. However, existing methods for learning context-based word embeddings typically …...

AbstractTree-structured neural network architectures for sentence encoding draw inspiration from the approach to semantic composition generally seen in formal linguistics, and have shown empirical improvements over comparable sequence models by …...

AbstractDeep neural network models such as long short-term memory (LSTM) and tree-LSTM have been proven to be effective for sentiment analysis. However, sequential LSTM is a bias model wherein the …...

Although sentiment is conceptualized as a con- tinuous variable, most text-based sentiment analyses categorize texts into discrete senti- ment categories. Compared to discrete catego- rizations, continuous sentiment estimates pro- vide …...