Chatbot: Understanding Google Search 1/3, python, tf-idf, cosine_similarity ver. 4

Last Updated on November 28, 2021 by shibatau

I’m rewriting the post now.

0. Rewriting Now

This post is the one that I wrote a few years ago. I’m rewriting it now and have calculated some parts again in different ways. The scripts are here:

https://colab.research.google.com/drive/1HIfzr7aOZJ8czYW5CzQJlyVqYOXNHSXm?usp=sharing

I. TF_IDF and Cosine Similarity

TF_IDFとCosine Similaryは類似性を算出するもので、テキスト・マイニングやチャット・ボットで用いられます。

GoogleはTF_IDEを検索に使うことによって利用者数を飛躍的に増やしたそうです。

次の文書では、Google検索を例としてTF_IDFとCosaine Similarityがわかりやすく説明されています。

Tf-Idf and Cosine similarity

上記を読めばわかるのですが、私のように数学にうとく、日常言語プログラムの初学者という人のために補足して解説します。

II. Googleの利用者の増加

1998年と比べ2000年にの1日の平均検索数は6122倍で、それ以降も急速に増加しました。Pythonで単純な棒グラフにしました。

# import library for dataframe and plot
import pandas as pd
import matplotlib.pyplot as plt
# read comma separated data
rawtext = """Year,SearchPerDay
1998,9800
2000,60000000
2007,1200000000
2008,1745000000
2009,2610000000
2010,3627000000
2011,4717000000
2012,5134000000"""
# split lines
rawtext_splitlines = rawtext.splitlines()
# split string with ","
# convert to a dataframe
df1 = pd.DataFrame([sub.split(",") for sub in rawtext_splitlines])
# set the column labels to the values in the 1st row
df1.columns = df1.iloc[0]
# drop the first row
df2 = df1.reindex(df1.index.drop(0))
# convert object to numeric
df2.SearchPerDay = pd.to_numeric(df2.loc[:,'SearchPerDay'], errors='coerce')
print(df2)
# show bar plot to confirm the numeric
%matplotlib inline # if you use Jupyter Notebook
df2.plot('Year', 'SearchPerDay', title = "Google search per day", kind='bar')
plt.show()

III. Text Pre-Processing

Textを処理できるょうに、不要な記号を削除し単語にわけます。

# data
text1 = "The game of life is a game of everlasting learning."
text2 = "The unexamined life is not worth living."
text3 = "Never stop learning."

# cleaning text and lower casing all words
#  text1
for char in '-.,\n':
    text1=text1.replace(char,' ')
text1 = text1.lower()
# split returns a list of words delimited by sequences of whitespace (including tabs, newlines, etc, like re's \s) 
document1 = text1.split()

#  text2
for char in '-.,\n':
    text2=text2.replace(char,' ')
text2 = text2.lower()
# split returns a list of words delimited by sequences of whitespace (including tabs, newlines, etc, like re's \s) 
document2 = text2.split()

#  text3
for char in '-.,\n':
    text2=text2.replace(char,' ')
text3 = text3.lower()

# split returns a list of words delimited by sequences of whitespace (including tabs, newlines, etc, like re's \s) 
document3 = text3.split()

# show cleaned text
print(document1)
print(document2)
print(document3)

from collections import Counter
Counter(document1).most_common()
Counter(document2)
Counter(document3)

前処理できると、ライブラリcollectionsを利用して簡単に語の数を集計できます。