Hello Everyone,

Here is the list of packages needed for our Text Mining Lab Session scheduled for 6/29/2017 (2:00-5:00 p.m.)

Updates:

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• I have uploaded some poster examples of some past students. (Check the posters folder)
• For the guys intereted in the slack community, send me your email to ellfae@gmail and I will provide an invite
• If you have any other questions or technical problems, feel free to stop by Idea Lab Delta 701. I will be more than happy to assist.
• I may extend the python notebook based on the excellent questions you guys asked (e.g., more statistics, visuals, etc.)
• Lastly, good luck and enjoy your stay here.

Software:

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• Python 3 (coding will be done strictly using Python 3)
• Anaconda Environment (recommended but not mandatory) (https://www.continuum.io/downloads)
• Jupyter (http://jupyter.org/)
• Google's word2vec (Download the file... warning! it is really huge)(https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing)
• Gensim (https://radimrehurek.com/gensim/)
• Scikit Learn (http://scikit-learn.org/stable/) (get the latest version)
• Pandas (http://pandas.pydata.org/)
• Matplotlib (https://matplotlib.org/)
• NLTK (for stopwords) (http://www.nltk.org/)

Computing Resources:

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• Operating System: Preferably Linux or MacOS (Windows break but you can try it out)
• RAM: 4GB
• Disk Space: 8GB (mostly to store word embeddings)

Test:

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Once you have installed all the necessary packages, you can test to see if everything is working by running the following python code:

import logging
logging.root.handlers = []  # Jupyter messes up logging so needs a reset
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
from smart_open import smart_open
import pandas as pd
import numpy as np
from numpy import random
import gensim
import nltk
from sklearn.cross_validation import train_test_split
from sklearn import linear_model
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.metrics import accuracy_score, confusion_matrix
import matplotlib.pyplot as plt
from gensim.models import Word2Vec
from sklearn.neighbors import KNeighborsClassifier
from sklearn import linear_model
from nltk.corpus import stopwords
%matplotlib inline

If you have any further questions please feel free to contact me at [email protected]

Have Fun,

Elvis Saravia (Text Mining TA)