Sentiment Analysis via Deep Hybrid Textual-Crowd Learning Model

Abstract

Crowdsourcing technique provides an efficient platform to employ human skills in sentiment analysis, which is a difficult task for automatic language models due to the large variations in context, writing style, view point and so on. However, the standard crowdsourcing aggregation models are incompetent when the number of crowd labels per worker is not sufficient to train parameters, or when it is not feasible to collect labels for each sample in a large dataset. In this paper, we propose a novel hybrid model to exploit both crowd and text data for sentiment analysis, consisting of a generative crowdsourcing aggregation model and a deep sentimental autoencoder. Combination of these two sub-models is obtained based on a probabilistic framework rather than a heuristic way. We introduce a unified objective function to incorporate the objectives of both sub-models, and derive an efficient optimization algorithm to jointly solve the corresponding problem. Experimental results indicate that our model achieves superior results in comparison with the state-of-the-art models, especially when the crowd labels are scarce.