tf-idf should help since it combines
- number of times a word appears in a document (i.e. each book)
- number of times a word appears in the set of documents (a.k.a. corpus)
If a word appears a lot in a document but not so much in the corpus, it is likely characteristic of the document and will have a high tf-idf score. If, on the other hand, a word appears frequently in a document and also frequently in the whole corpus, it is not very characteristic of such document and this does not have a high tf-idf score. The words with the highest tf-idf measures per document are the most relevant.
Stop word removal may be a step you want want to perform on your data before getting tf-idf measures for your documents, but you may want to try with and without stop words to compare performance.
EDIT:
To support what I mentioned in the comment re. not having to come up with the stopwords yourself, here's NLTK's English stopwords, which you can add to or remove from according to whatever you want to implement:
>>> import nltk
>>> from nltk.corpus import stopwords
>>> stopwords.words('english')
['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you',
'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his',
'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself',
'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which',
'who', 'whom', 'this', 'that', 'these', 'those', 'am', 'is', 'are',
'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having',
'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if',
'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for',
'with', 'about', 'against', 'between', 'into', 'through', 'during',
'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down',
'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then',
'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any',
'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no',
'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's',
't', 'can', 'will', 'just', 'don', 'should', 'now']