By this you can expect better segregation of sentiment. Now, to improve my model I made an new dictionary for some words like not great - which falls under a Neutral Sentiment(but I don't have one), If these words are found then that tweet is pushed to negative. There was an increase of 2-3% of accuracy, which was significant for me. In my Scenario, I had to remove all the Non-English as my model wasn't trained to handle such words.
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