CrowdTSC: Crowd-based Neural Networks for Text Sentiment Classification
This addresses sentiment classification for content analysis, offering a hybrid approach that is incremental in combining crowdsourcing with existing neural methods.
The paper tackles the problem of text sentiment classification by incorporating human-collected keywords into neural networks, resulting in CrowdTSC outperforming state-of-the-art models on public datasets.
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.