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A rule of thumb is to train a machine learning model you need data points, as much as ten times your model parameters.


That would depend on the exact goal of the task and the specifics of the dataset, but in general I would say that it's always better to use the information specifically provided with the data if it's relevant for the task. In this case the rating for the product is indeed very likely to reflect the sentiment of the text, so I would go with it. Notice that ...


Yes outlier affect naive bayes. If a word that comes in testing data that has not been seen in training leads to zero probab of that particular word in the particular class. And we know in naive bayes we multipy probab of words lying in that particular class and results zero..that leads to wrong result so thats why we have laplace smoothing in naive bayes.. ...

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