Should I use a cleaned labeled data for sentiment analysis?
The OP asks "Does cleaning of data before labeling make any difference?" - that's an empirical question... one that should be investigated by EDA of your data. 

In some cases twitter-specific conventions can be highly indicative of a specific class (e.g., http:// associated with spam/advertising tweets) or sentiment (e.g., :-) emoticon associated with positive valence). Similarly, as I discuss in this post, stop words can be great features to keep in certain types of text models. My support for the above answer is based on an (unpublished) project I did that involved about 10 people hand-scoring 50k tweets stratified across major industries (food, travel, electronics, CPG, etc.) and brands (McDonalds, Southwest Airlines, iPhone, Tide PODs). 

My advice, create feature extractors for any textual feature that you believe has a theoretical or logical justification for being good indicator of text polarity. Then empirically test each feature to determine whether it significantly improves classification accuracy. Keep those that are in your model; save the other feature extractors for a rainy day.