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# Represents a dictionary as an instance (note: cannot yet cast bact to dict type, simple access <InstanceDict>.d | |
class InstanceDict: | |
def __init__(self, d): | |
self.d = d | |
def __getattribute__(self, item): | |
return self.__getattr__(item) | |
def __getattr__(self, key): | |
val = self.d[key] |
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# You need to install scikit-learn: | |
# sudo pip install scikit-learn | |
# | |
# Dataset: Polarity dataset v2.0 | |
# http://www.cs.cornell.edu/people/pabo/movie-review-data/ | |
# | |
# Full discussion: | |
# https://marcobonzanini.wordpress.com/2015/01/19/sentiment-analysis-with-python-and-scikit-learn |
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# you need to install Biopython: | |
# pip install biopython | |
# Full discussion: | |
# https://marcobonzanini.wordpress.com/2015/01/12/searching-pubmed-with-python/ | |
from Bio import Entrez | |
def search(query): | |
Entrez.email = '[email protected]' |
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# Print most common words in a corpus collected from Twitter | |
# | |
# Full description: | |
# http://marcobonzanini.com/2015/03/02/mining-twitter-data-with-python-part-1/ | |
# http://marcobonzanini.com/2015/03/09/mining-twitter-data-with-python-part-2/ | |
# http://marcobonzanini.com/2015/03/17/mining-twitter-data-with-python-part-3-term-frequencies/ | |
# | |
# Run: | |
# python twitter_most_common_words.py <filename.jsonl> |
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consumer_key = 'your-consumer-key' | |
consumer_secret = 'your-consumer-secret' | |
access_token = 'your-access-token' | |
access_secret = 'your-access-secret' |
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# You need to install scikit-learn: | |
# sudo pip install scikit-learn | |
# | |
# Dataset: Polarity dataset v2.0 | |
# http://www.cs.cornell.edu/people/pabo/movie-review-data/ | |
# | |
# Full discussion: | |
# https://marcobonzanini.wordpress.com/2015/01/19/sentiment-analysis-with-python-and-scikit-learn | |
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# This code uses Biopython to retrieve lists of articles from pubmed | |
# you need to install Biopython first. | |
# If you use Anaconda: | |
# conda install biopython | |
# If you use pip/venv: | |
# pip install biopython | |
# Full discussion: |