Predictive Modeling of Multiple Items
Overall classification is generally better when the decision rules of each component classifier differ and provide complimentary information.

So the question becomes: Can you set up your component classifiers so that their decision rules are different and compliment one another based on the feature space? e.g. Does Post 1 have a significantly different feature space than Post 2?  etc. If so, the ensemble approach should be beneficial.   

Which technique?  If you can highly train each classifier and make it an expert in different regions of the feature space, try models:


mixture model
mixture distribution
gating subsystem
winner take all.