Understanding the bag of words in NLP | by Mathanraj Sharma | Towards Data Science

Natural language processing is an important branch of Artificial intelligence where many interesting and important pieces of research are going on. As a machine learning enthusiast, it is important to understand the sub-processes of NLP.
Here I would like to share one of the buzz words used in NLP with (link to notebook attached at the bottom) code examples. “BAG OF WORDS!!!”.
Bag of words is a method that is used to find out the important topics in a text (paragraph). What are the topics? let’s say you are reading the below paragraph,
As a pet, cat is a very useful animal and helps in protecting or saving our rashan from rats. The offspring of a cat is called as kitten, it is a smaller and a cuter version of a cat. Cat has got four thin, short and sturdy limbs that helps it in walking, running and jumping for long distances.It’s bright eyes help it in seeing long distances and also help during the dark. Cats are found all over the world. There is no place without a cat. Sometimes a cat can be mistaken for a tiger cub, because of its extreme similarities with it.A cat’s body is completely covered with soft and beautiful fur. Cats make meaw meaw sound. God has provided cats with soft shoes or pads, which help a cat in walking without making a sound.
[Text Credits: https://www.atozessays.com/animals/essay-on-cat/]
As a human when you are reading this, you know this paragraph is about the cat. Cat is an important topic in the above paragraph. But,
how does a machine suppose to figure this out?
how can you tell your model cat is an important topic in this paragraph?
"The more frequent a word, the more important it might be!!!"
This where the bag of words comes into play!!!
First, create the tokens of the paragraph using tokenization, tokens can be anything that is a part of a text, i.e words, digits, punctuations or special charactersApply necessary text preprocessing steps to filter out the good tokens, such as lowercasing words, lemmatization/stemming (bring the words to their root form), removing stop words and punctuations and etc.Count up the occurrences of each token to find the most common words.
First, create the tokens of the paragraph using tokenization, tokens can be anything that is a part of a text, i.e words, digits, punctuations or special characters
Apply necessary text preprocessing steps to filter out the good tokens, such as lowercasing words, lemmatization/stemming (bring the words to their root form), removing stop words and punctuations and etc.
Count up the occurrences of each token to find the most common words.
# declare your text hereparagraph = "As a pet, cat is a very useful animal and helps in protecting or saving our rashan from rats........................."# tokenize the paragraph using word_tokenize,return tokenstokens = word_tokenize(paragraph)# change the tokens to lower caselower_tokens = [token.lower() for token in tokens]# Retain alphabetic words: alpha_only, eliminate punctions and special charactersalpha_only = [t for t in lower_tokens if t.isalpha()]#remove all stop wordsstop_words = set(stopwords.words('english'))filtered_tokens = [token for token in alpha_only if not token in stop_words]# lemmatize the words to bring them to their root formwordnet_lemmatizer = WordNetLemmatizer()lemmatized_tokens = [wordnet_lemmatizer.lemmatize(token) for token in filtered_tokens]# create bag of wordsbag_of_words = Counter(lemmatized_tokens)# print the top 5 most common wordsprint(bag_of_words.most_common(5))Output:[('cat', 11), ('help', 5), ('walking', 2), ('long', 2), ('without', 2)]
By looking at the output you can simply say, this is a text about cats since it is the most occurring word in the text. Now you can proceed to your next steps of NLP using this bag of words as features.
This is just one way of doing it, I have only added simple preprocessing steps for simplicity. But the preprocesses step will vary from case to case. Read similar works and fine-tune preprocessing steps for your case.
You can have a look at the notebook for this example here. I hope this article is helpful to you!!!