We investigate the value of feature en gineering and neural network models for predicting successful writing. Similar to previous work, we treat this as a binary classification task and explore new strate gies to automatically learn representations from book contents. We evaluate our fea ture set on two different corpora created from Project Gutenberg books. The first presents a novel approach for generating the gold standard labels for the task and the other is based on prior research. Us ing a combination of hand-crafted and re current neural network learned representa tions in a dual learning setting, we obtain the best performance of 73.50% weighted Fl-score.
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