  Sentiment classification is a fundamental task in content analysis. Although
deep learning has demonstrated promising performance in text classification
compared with shallow models, it is still not able to train a satisfying
classifier for text sentiment. Human beings are more sophisticated than machine
learning models in terms of understanding and capturing the emotional
polarities of texts. In this paper, we leverage the power of human intelligence
into text sentiment classification. We propose Crowd-based neural networks for
Text Sentiment Classification (CrowdTSC for short). We design and post the
questions on a crowdsourcing platform to collect the keywords in texts.
Sampling and clustering are utilized to reduce the cost of crowdsourcing. Also,
we present an attention-based neural network and a hybrid neural network, which
incorporate the collected keywords as human being's guidance into deep neural
networks. Extensive experiments on public datasets confirm that CrowdTSC
outperforms state-of-the-art models, justifying the effectiveness of
crowd-based keyword guidance.
