NLP and sentiment analysis without labels?

Part 1 (2017)
2

For the first problem, you can try training a sentiment classification model on a public data set where labels are available, and using it to generate predictions for your data. You won’t have an accuracy metric this way, though, and the model might perform poorly if the domains are very different. So it might be best to just spend a few days labeling randomly selected comments.

For the second problem (detecting names), I would consider using an NLP package such as spaCy to extract entities from the text. It’s not using deep learning, but it might be a simple solution for your needs. This can look as simple as:

import spacy
nlp = spacy.load('en')
def has_name(text):
    doc = nlp(my_text)
    for ent in doc.ents:
        if ent.ent_type_ == 'PERSON':
            return True # doc contains a person's name
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