Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
Abstract
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 exploits from characterto sentence-level information to perform sentiment analysis of short texts. We apply our approach for two corpora of two different domains: the Stanford Sentiment Treebank (SSTb), which contains sentences from movie reviews; and the Stanford Twitter Sentiment corpus (STS), which contains Twitter messages. For the…
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Topics
Keywords
- Treebank
- Computer science
- Sentiment analysis
- Artificial intelligence
- Natural language processing
- Convolutional neural network
- Sentence
- Deep learning
UN Sustainable Development Goals
- Quality Education
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