@kida2002.bsky.social

@kida2002.bsky.social

78 Followers 307 Following

I am not a robot, I just don't post that much

Machine learning approaches are not without drawbacks. First, they require (often extensive) training data which are, as with validated sentiment lexicons, sometimes troublesome to acquire. Second, they depend on the training set to represent as many features as possible (which often, they do not – especially in the case of the short, sparse text of social media). Third, they are often more computationally expensive in terms of CPU processing, memory requirements, and training/classification time (which restricts the ability to assess sentiment on streaming data). Fourth, they often derive features “behind the scenes” inside of a black box that is not (easily) humaninterpretable and are therefore more difficult to either generalize, modify, or extend (e.g., to other domains).