The Profile of Mood States (POMS) and its variations have been used in many real world contexts to assess individuals behavior and measure mood. Social Networks such as Twitter and Facebook are considered precious research sources of collecting user mood measurements. In particular, we are inspired in this paper, by recent work on the prediction of the stock market movement from attributes representing the public mood collected from Twitter. In this paper, we build a new prediction model for the same stock market problem based on single models combination. Our proposed approach to build such model is simultaneously promoting performance and interpretability. By interpretability, we mean the ability of a model to explain its predictions. We implement our approach using Ant Colony Optimization algorithm and we use customized Bayesian Classifiers as single models. We compare our approach against the best Bayesian single model, model learned from all the available data, bagging and boosting algorithms. Test results indicate that the proposed model for stock market prediction performs better than those derived by alternatives approaches.
展开▼
