def exp011():
"""
The relationship might be better represented with word count on a log
scale. This plot shows that the two values are strongly correlated.
"""
x, y = zip(*getWordCountAffectCount(True))
plt.ylabel("affect ratio")
plt.xlabel("comment length (log number of words)")
_, pearP = pearsonr(x, y)
plt.scatter(x, y, lw=0)
plt.legend(loc=3)
plt.savefig("../experiments/exp011.pdf", format="pdf")
def exp01():
"""
Experiment 1:
Make scatter plot about comment length vs. affect ratio. This plot shows
that increasing comment length does result in a lower affect ratio.
However, if the change in affect ratio was caused by a change in comment
length, then we should be able to predict comment length.
"""
x, y = zip(*getWordCountAffectCount())
plt.scatter(x, y)
plt.ylabel("affect ratio")
plt.xlabel("comment length (# words)")
_, pearP = pearsonr(x, y)
label = "p < 0.0001" if pearP < 0.0001 else "p = %0.4f" % pearP
plt.scatter(x, y, label=label)
plt.legend(loc=3)
plt.savefig("../experiments/exp01.pdf", format="pdf")