Real utility of small accuracy improvements in sentiment classifiers I have lately been reading papers regarding Sentiment Analysis, where most researches report that their improvements made them achieve an increase of 1~2%, or even 0.5% in accuracy compared to baseline (non-trivial) methods.   
Of course, I understand that such an increase (even if small, if statistically significant) is a good thing, but what are the advantages, in terms of application/utility of such a small improvement ? Is there any application/practical use that could directly benefit from such a small improvement ? If such an application exists, how to know which is the minimal accuracy needed for it?    
(Similar question is posted here, but I am asking for a more practical view rather than statistical)      
 A: Such improvements matter if you want to publish or win a Kaggle competition, but are less important for practical applications.
Obviously, more accurate is better, so if you can use something that is more accurate, better for you. But, first, there are implementation costs, for example, Netflix didn't use the algorithm that won their competition and stayed with less accurate, simpler one because the change wasn't worth the additional costs. Second, the fact that some algorithm, trained on some particular training set has achieved some result on a particular test set does not mean that it will do the same for any dataset, or even that the performance won't change over time. Moreover, there is ongoing debate if the algorithms described in the literature aren't overfitting to the test sets that are used by everyone (see Recht et al, arXiv:1806.00451).
So usually you won't re-design your machine learning pipeline just because the "better" algorithm was described in the literature. On another hand, you may still consider testing it, or it may inspire some improvements in ongoing or future projects, but only after assessing the cost-effectiveness (where cost is time, computational resources needed etc).
