Why do we have to remove most common words for text analysis? I am trying to do sentiment analysis the task is to classify racist tweets from other tweets. And I have read many articles and many have mentioned to remove the most common 10 words from the column because their presence will not of any use in classification of our text data.

So these are my top 10 most common words on my dataset.

[('love', 4271),
 ('day', 3572),
 ('amp', 2709),
 ('happy', 2651),
 ('u', 1840),
 ('time', 1771),
 ('im', 1770),
 ('life', 1756),
 ('like', 1700),
 ('today', 1591)]


If I remove these will my classification model be more accurate?

Similarly they are also recommending to remove the top 10 rare words from the column.

I want to know why? Any help
The simplest way to explain why it may be advantageous to remove the most common words is that they don't give us much information. In your case of classifying racist tweets, words like "and", "a", "the", etc. don't help the classifier and may act as noise which negatively impacts performance.

I would not say that the removal of the n most popular words will guarantee that the model will be more accurate, but it is a parameter you can explore. Outside of complete removal of the most common words, you may want to look into techniques like subsampling.