Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning by Benjamin Bengfort & Rebecca Bilbro & Tony Ojeda

Author:Benjamin Bengfort & Rebecca Bilbro & Tony Ojeda [Bengfort, Benjamin]
Language: eng
Format: epub
Publisher: O'Reilly Media
Published: 2018-06-10T23:00:00+00:00

... # First make the matrices # By frequency mtx = matrix(text,cast) # Now create the plots fig, ax = plt.subplots() fig.suptitle('Character Co-occurrence in the Wizard of Oz', fontsize=12) fig.subplots_adjust(wspace=.75) n = len(cast) x_tick_marks = np.arange(n) y_tick_marks = np.arange(n) ax1 = plt.subplot(121) ax1.set_xticks(x_tick_marks) ax1.set_yticks(y_tick_marks) ax1.set_xticklabels(cast, fontsize=8, rotation=90) ax1.set_yticklabels(cast, fontsize=8) ax1.xaxis.tick_top() ax1.set_xlabel("By Frequency") plt.imshow(mtx, norm=matplotlib.colors.LogNorm(), interpolation='nearest', cmap='YlOrBr')

To create the alphabetic view of the co-occurrence plot, we begin by alphabetizing the list of characters and specifying that we want to work with the second subplot, (122), and add the axes elements much in the same way as for the first subplot:

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