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Text Clustering and Visualize Similarity in Python

I'm analyzing a book of 1167 pages (txt file). So far I've done preprocessing of the data (cleaning, removing punctuation, stop word removing, tokenization).
Now How can I cluster the text and visualize the similarity (plot the cluster)
For example -
text1 = "there is one unshakable conviction that people—whatever the degree of development of their understanding and whatever the form taken by the factors present in their individuality for engendering all kinds of ideal"
text2 = "That is why I now also, in setting forth on this venture quite new for me, namely authorship, begin by pronouncing this invocation"
In my task, I divided whole book into chapters. tex1 is chapter one text2 is chapter 2 and so on. Now I wanna compare chapter 1 and 2 like this.
Example code that I used for data pre processing:
# split into words
from nltk.tokenize import word_tokenize
tokens = word_tokenize(pages[1])
# convert to lower case
tokens = [w.lower() for w in tokens]
# remove punctuation from each word
import string
table = str.maketrans('', '', string.punctuation)
stripped = [w.translate(table) for w in tokens]
# remove remaining tokens that are not alphabetic
words = [word for word in stripped if word.isalpha()]
# filter out stop words
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
words = [w for w in words if not w in stop_words]

Unstructured data, NLP Lemmatize Book Review

Here I have m trying to read the content let's say 'book1.txt' and here I have to remove all the special characters and punctuation marks and word tokenise the content using nltk's word tokeniser.
Lemmatize those token using wordnetLemmatizer
And write those token into csv file one by one.
Here is the code I m using which obviously is not working but just need some suggestion on this please.
import nltk
from nltk.stem import WordNetLemmatizer
import csv
from nltk.tokenize import word_tokenize
file_out=open('data.csv','w')
with open('book1.txt','r') as myfile:
for s in myfile:
words = nltk.word_tokenize(s)
words=[word.lower() for word in words if word.isalpha()]
for word in words:
token=WordNetLemmatizer().lemmatize(words,'v')
filtered_sentence=[""]
for n in words:
if n not in token:
filtered_sentence.append(""+n)
file_out.writelines(filtered_sentence+["\n"])

There's some issues here, most notably with the last 2 for loops.
The way you are doing it made it write it as follows:
word1
word1word2
word1word2word3
word1word2word3word4
........etc
I'm guessing that is not the expected output. I'm assuming the expected output is:
word1
word2
word3
word4
........etc (without creating duplicates)
I applied the code below to a 3 paragraph Cat Ipsum file. Note that I changed some variable names due to my own naming conventions.
import nltk
nltk.download('punkt')
nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from pprint import pprint
# read the text into a single string.
with open("book1.txt") as infile:
text = ' '.join(infile.readlines())
words = word_tokenize(text)
words = [word.lower() for word in words if word.isalpha()]
# create the lemmatized word list
results = []
for word in words:
# you were using words instead of word below
token = WordNetLemmatizer().lemmatize(word, "v")
# check if token not already in results.
if token not in results:
results.append(token)
# sort results, just because :)
results.sort()
# print and save the results
pprint(results)
print(len(results))
with open("nltk_data.csv", "w") as outfile:
outfile.writelines(results)

create variable name based on text in the sentence

I have a list of sentences. Each sentence has to be converted to a json. There is a unique 'name' for each sentence that is also specified in that json. The problem is that the number of sentences is large so it's monotonous to manually give a name. The name should be similar to the meaning of the sentence e.g., if the sentence is "do you like cake?" then the name should be like "likeCake". I want to automate the process of creation of name for each sentence. I googled text summarization but the results were not for sentence summarization but paragraph summarization. How to go about this?

This sort of task is used for natural language processing. You can get a result similar to what you want by removing Stop Words. Bases on this article, you can use the Natural Language Toolkit for dealing with the stop words. After installing the libray (pip install nltk), you can do something around the lines of:
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import string
# load data
file = open('yourFileWithSentences.txt', 'rt')
lines = file.readlines()
file.close()
stop_words = set(stopwords.words('english'))
for line in Lines:
# split into words
tokens = word_tokenize(line)
# remove punctuation from each word
table = str.maketrans('', '', string.punctuation)
stripped = [w.translate(table) for w in tokens]
# filter out stop words
words = [w for w in words if not w in stop_words]
print(f"Var name is {''.join(words)}")
Note that you can extend the stop_words set by adding any other words you might want to remove.

Remove Stop Words Python

So I am reading in a csv file and the getting the words in it. I am trying to remove stop words. Here is my code.
import pandas as pd
from nltk.corpus import stopwords as sw
def loadCsv(fileName):
df = pd.read_csv(fileName, error_bad_lines=False)
df.dropna(inplace = True)
return df
def getWords(dataframe):
words = []
for tweet in dataframe['SentimentText'].tolist():
for word in tweet.split():
word = word.lower()
words.append(word)
return set(words) #Create a set from the words list
def removeStopWords(words):
for word in words: # iterate over word_list
if word in sw.words('english'):
words.remove(word) # remove word from filtered_word_list if it is a stopword
return set(words)
df = loadCsv("train.csv")
words = getWords(df)
words = removeStopWords(words)
On this line
if word in sw.words('english'):
I get the following error.
exception: no description
Further down the line I am going to try to remove punctuation, any pointers for that too would be great.
Any help is much appreciated.
EDIT
def removeStopWords(words):
filtered_word_list = words #make a copy of the words
for word in words: # iterate over words
if word in sw.words('english'):
filtered_word_list.remove(word) # remove word from filtered_word_list if it is a stopword
return set(filtered_word_list)

Here is a simplified version of the problem, without Panda. I believe the issue with the original code is with modifying the set words while iterating over it. By using a conditional list comprehension, we can test for each word, creating a new list, and ultimately converting it into a set, as per the original code.
from nltk.corpus import stopwords as sw
def removeStopWords(words):
return set([w for w in words if not w in sw.words('english')])
sentence = 'this is a very common english sentence with a finite set of words from my imagination'
words = set(sentence.split())
print(removeStopWords(words))

Change removeStopWords function to the following:
def getFilteredStopWords(words):
list_stopWords=list(set(sw.words('english')))
filtered_words=[w for w in words if not w in list_stopWords# remove word from filtered_words if it is a stopword
return filtered_words

def remmove_stopwords(sentence):
list_stop_words = set(stopwords.words('english'))
words = sentence.split(' ')
filtered_words = [w for w in words if w not in list_stop_words]
sentence_list = ' '.join(w for w in filtered_words)
return sentence_list

How to add more stopwords in nltk list?

I have the following code. I have to add more words in nltk stopword list. After i run thsi, it does not add the words in the list
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
import string
stop = set(stopwords.words('english'))
new_words = open("stopwords_en.txt", "r")
new_stopwords = stop.union(new_word)
exclude = set(string.punctuation)
lemma = WordNetLemmatizer()
def clean(doc):
stop_free = " ".join([i for i in doc.lower().split() if i not in new_stopwords])
punc_free = ''.join(ch for ch in stop_free if ch not in exclude)
normalized = " ".join(lemma.lemmatize(word) for word in punc_free.split())
return normalized
doc_clean = [clean(doc).split() for doc in emails_body_text]

Don't do things blindly. Read in your new list of stopwords, inspect it to see that it's right, then add it to the other stopword list. Start with the code suggested by #greg_data, but you'll need to strip newlines and maybe do other things -- who knows what your stopwords file looks like?
This might do it, for example:
new_words = open("stopwords_en.txt", "r").read().split()
new_stopwords = stop.union(new_words)
PS. Don't keep splitting and joining your document; tokenize once and work with the list of tokens.