You don't have to now. But what if you were to change the problem a bit, you'd need to reinvent those "elementary text cues", right? With deep learning (or, mor

You don't have to now. But what if you were to change the problem a bit, you'd need to reinvent those "elementary text cues", right? With deep learning (or, more generally, representation learning) you can simply change the training data and reuse the rest of your algorithm. Jure Leskovec has a paper, node2vec, which describes this well: > A typical solution in- volves hand-engineering domain-specific features based on expert knowledge. Even if one discounts the tedious effort required for feature engineering, such features are usually designed for specific tasks and do not generalize across different prediction tasks. An alternative approach is to learn feature representations by solving an optimization problem [4]. The challenge in feature learn- ing is defining an objective function, which involves a trade-off in balancing computational efficiency and predictive accura