How to create a training set and classify them as positive or negative
The only way to obtain a high-quality dataset in your specific domain is to do it manually. There exists no other method that can give you the sentiment labels for texts in arbitrary domains. If there would exists such a method, why would you even bother to create your own model. 

You should probably find/hire people that can do this work for you. Adding the sentiment meta-data to the texts could be as easy as creating an additional column in excel. But you could also smoothen the process by creating a little app that shows the sentence and let the user decide what sentiment it is (e.g. by swiping to left or right). Find a bunch of people that are willing to classify 10 sentences a day and your dataset will grow steadily. Even better, apply a little gamification and give people points or a rank and give an award to the best performing user to boost their performance. 

Keep in mind that if these texts are really domain-specific you probably need domain-experts to do the classification. 

When having enough training data you could learn a (simple) that predicts the output. Then you could let the human classifiers focus on the sentences with low confidence, assuming that high confidence automatic classifications are correct.