In Stanford sentiment analysis do we have an option for marking specific/custom words as positive[based on our requirements].
Analysing tweets is giving a negative trend due to the business terms used. Can we handle it to neutralize the negative output due to these words by adding our custom dictionary ?
The cleanest way to do this would be to retrain the sentiment model. Acquire the sentiment training data and manually modify the labels for the words you are concerned about. There are very basic instructions for training on another Stanford Sentiment page. Then use this trained model as you wish!
A very dirty but possibly faster solution would be to modify the trees you get from the standard model after the fact. For example, you'd search an analyzed tree for words of interest and manually modify their sentiment label. Then apply some heuristic in order to propagate this modification up the tree and possibly alter the sentiment of the whole sentence.