Chapter 7: Topic Modeling

Activity 15: Loading and Cleaning Twitter Data

Solution:

1. Import the necessary libraries:

   import langdetect
   import matplotlib.pyplot
   import nltk
   import numpy
   import pandas
   import pyLDAvis
   import pyLDAvis.sklearn
   import regex
   import sklearn

2. Load the LA Times health Twitter data (latimeshealth.txt) from https://github.com/TrainingByPackt/Applied-Unsupervised-Learning-with-Python/tree/master/Lesson07/Activity15-Activity17:

   Note

   Pay close attention to the delimiter (it is neither a comma nor a tab) and double-check the header status.

   path = '<Path>/latimeshealth.txt'
   df = pandas.read_csv(path, sep="|", header=None)
   df.columns = ["id", "datetime", "tweettext"]

3. Run a quick exploratory analysis to ascertain the data size and structure:

   def dataframe_quick_look(df, nrows):
   print("SHAPE:\n{shape}\n".format(shape=df.shape))
   print("COLUMN NAMES:\n{names}\n".format(names=df.columns))
   print("HEAD:\n{head}\n".format(head=df.head(nrows)))
   
   dataframe_quick_look(df, nrows=2)

   The output...