Paper: Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts

ACL ID C14-1008
Title Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

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 that combine the small text content with prior knowledge and use more than just bag-of-words. In this work we propose a new deep convolutional neural network that ex- ploits from character- to sentence-level information to perform sentiment analysis of short texts. We apply our approach for two corpora of two different domains: the Stanford Sentiment Tree- bank (SSTb), which contains sentences from movie reviews; and the Stanford Twitter Sentiment corpus (STS), which contains Twitter messages. For the SSTb corpus, our approach achieves state-of-the-art results for single sen...