<jupyter_start><jupyter_text>Importing the Libraries<jupyter_code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
from textblob import TextBlob as tb<jupyter_output><empty_output><jupyter_text>Categories obtained from Pattern Analyzer<jupyter_code>p_positive1 = 20.0 
p_negative1 = 10.0
p_neutral1  = 30.0
p_facts1    = 0.0
p_abusive1  = 10.0
p_others1  =  30.0<jupyter_output><empty_output><jupyter_text>Categories obtained from Naive Bayes<jupyter_code>p_positive2 = 20.0 
p_negative2 = 10.0
p_neutral2  = 10.0
p_facts2    = 40.0
p_abusive2  = 10.0
p_others2  =  10.0<jupyter_output><empty_output><jupyter_text>Visualizing the categories<jupyter_code>plt.bar([1,5,9,13,17,21],[p_positive1, p_negative1, p_neutral1, p_facts1,p_abusive1,p_others1], label="Pattern Analyzer")
plt.bar([2,6,10,14,18,22],[p_positive2, p_negative2, p_neutral2, p_facts2,p_abusive2,p_others2], label="Naive Bayes", color='g')
plt.legend()
plt.xlabel('Categories')
plt.ylabel('Count')
plt.title('Working of Pattern Analyzer VS Naive Bayes')
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11705989110707804, 'fraction_numerical': 0.07078039927404718, 'mean_word_length': 7.824, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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