raffg / Harry_potter_nlp
Harry Potter and the Allocation of Dirichlet
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NLP on the Books of Harry Potter
This repo demonstrates a collection of NLP tasks all using the books of Harry Potter for source documents. Individual tasks can be read about here:
- Topic modeling with Latent Dirichlet Allocation
- Regular Expression case study
- Extractive text summarization
- Sentiment analysis
Instructions for BasicNLP class (basic_nlp.py)
Functions of the class are topic modeling with LDA, document summarization, and sentiment analysis.
- Initialize the class with a list of documents and an optional list of document titles, for example:
texts = ['this is the first document', 'this is the second document', 'this is the third document']
titles = ['doc1', 'doc2', 'doc3']
nlp = BasicNLP(texts, titles)
-
LDA:
- Create an elbow plot and print the coherence scores by specifying the number of topics to include, with:
nlp.compute_coherence(start=5, stop=20, step=3)
- Set the number of topics to use in the model with:
nlp.set_number_of_topics(10)
- View the clusters (only available in Jupyter notebook):
import pyLDAvis pyLDAvis.enable_notebook() vis = nlp.view_clusters() pyLDAvis.display(vis)
- Get the vocabulary for each topic in the LDA model with (topics can be 'all', a list of integers, or a single integer):
nlp.get_topic_vocabulary(topics='all', num_words=10)
- Get the documents most highly associated with the given topics with:
nlp.get_representative_documents(topics='all', num_docs=1)
- Get the sentence summaries of the documents most highly associated with the given topics with:
nlp.get_representative_sentences(topics='all', num_sentences=3)
- Provide a name for an LDA topic (if preferred over the numbering system) with:
nlp.name_topic(topic_number=1, topic_name='My topic')
- Create an elbow plot and print the coherence scores by specifying the number of topics to include, with:
-
Document summarization:
Get the sentence summaries of the requested documents with:
nlp.get_document_summaries(documents='all', num_sent=5)
-
Sentiment analysis:
Get the sentiment scores (compound, positive, neutral, negative) for the requested documents with:
nlp.get_sentiment(documents='all')
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