Various machine learning techniques are beneficial and help in achieving great results. There are various types of structured and unstructured data available for various purposes. Generally, numeric values are provided for machine learning algorithms. In other words, numeric data is used to train machine learning algorithms so that it works to achieve the major purpose of any application. Numeric data is sometimes directly available or is obtained using various statistical techniques. But as technology progresses with time, several new techniques have been invented to train machine learning algorithms and derive meaningful insights. One such technique is the bag of words using python. As the name suggests, this is a technique to deal with a textual form of data. But, in the end, the text is finally converted to numbers.
Bag of words (BOW) is a technique used for textual content. In other words, this technique is used to draw essential data from a large text. The essential data is generally words and sentences. After extracting unique data, a collection is made, and the word count representing a particular sentence is maintained. One major advantage of this technique is that it is straightforward to understand. Once the concept is understood, it is easy to code it in Python.
Limitations of textual content
A text document consists of several sentences. As the number of sentences is high, the list of words is also huge. This is in the case of one basic text document. In real-time, there are thousands of text documents that are used to train machine learning algorithms. These documents consist of letters, words, and sentences.
Textual data cannot be used for machine learning algorithms. It is essential to convert them into numbers. Direct conversion without using any technique is extremely time-consuming. It is a next to impossible task. Therefore, the bag of words technique is used so that textual data can be converted into numbers and used in various machine learning algorithms.
Where is the bag of words using python used?
Bag of words is a modern technique widely used in artificial intelligence, machine learning and data science fields. Given below are the most famous applications of this technique.
- Bag of words using python is used to pull out important information from pages of textual content such as a text document. After extracting such information, the textual data is converted into numbers for machine learning algorithms.
- When many text files and documents are present, this technique can be used to segregate them based on specific criteria. This facilitates easy extraction and processing.
- It is also being used in manipulating speech or text automatically with the help of software. This technique is known as Natural Language Processing (NLP).
Understanding the concept of the bag of words
Let’s consider three sentences that are extracted from a particular text document.
I like to eat chocolate.
Did you go outside to eat ice-cream?
Paul and I eat ice-cream.
As these are sentences, we cannot apply statistics as no numeric data is present. Therefore, to use statistical techniques, it is necessary to convert this textual data into numbers.
Given below are the steps to perform the bag of words technique.
Step 1: Arrange the words in the sentences individually – Tokenize
For demonstration purposes, we have taken only the above three sentences. In the real world, thousands and millions of sentences will be extracted. After extraction of the sentences, take the words and write them one below the other. Make sure to write all the words, including the one-letter words. This will help us to find the occurrence of each word in each sentence. In addition to this, breaking the sentence into individual units will help to analyze the data better. In Python, an array of individual words is formed.
Step 2: Find the occurrence
After splitting the sentences into individual word units, determine each word’s number in all three sentences. Write down the number to the right of each word. Take your time and count the number of occurrences properly. Remove all repetitions from the word list and ensure that there is a unique set of words.
Step 3: Sort the words based on their occurrence
After calculating the words’ occurrence, it is important to find out the most occurred word in all the three sentences. Therefore, sort the data in descending order by placing the most occurred word initially, and the least occurred word towards the end.
Step 4: Build the bag of words model
The last step is to build the bag of words model. This is done with the help of a matrix. In general, a matrix is the representation of data in a row and column format. The rows consist of the sentences which are obtained from the textual content. The columns consist of the most occurred words as analyzed during the third step.
Let’s construct the matrix for our sentences.
Here, the matrix is constructed using the most frequently occurred words in all three sentences only. Let’s understand its creation. The first row comprises sentence 1. The word “eat” occurs only once in the first sentence. Therefore, one is inserted for the word “eat” in sentence 1. The other places of the matrix are filled up similarly based on each sentence.
The above-given steps clearly explain the working and functionality of the bag of words technique. This functionality is applied in real-time using a trending programming language – Python. This can be done using arrays and various methods offered by Python.
As this technique is widely being used regularly, several frameworks and libraries are available, which support the bag of words technique. Therefore, there is no need to do the actual coding. The required libraries and other in-built files can be imported to use the technique.
The individual words of each sentence are converted into an array using Python. After the second step, the length of the vectors can be computed. This is done by adding up the most frequently occurred words in all three sentences. In this case, the length of the vectors is 9. The most frequently occurred words are considered to be vectors. These vectors will be used as input data for the machine learning algorithm to be trained.
Disadvantages of Bag of Words technique
Bag of words using python is a unique technique and is interesting to perform. But it suffers from the following disadvantages.
- In real-time, the list of sentences extracted from the text document is huge. As the number of sentences is more, the list of words also keeps increasing. This brings in several complications. Such complications might lead to misleading conclusions.
- This technique does not consider the actual meaning of the data which is extracted. As the actual meaning is ignored, there might be a problem in the working of the particular algorithm for which it is used.
Bag of words using Python is proven to be a useful technique if the theory behind the technique is thoroughly understood. But if the data set is huge, it might not work as expected. In addition to this, people with no coding knowledge might face difficulties. Although built-in libraries support the bag of words technique, it is essential to understand the concept to use the machine learning technique. The primary purpose of the bag of words is extracting information and classifying it in the right form. Therefore, this technique is useful if you possess good coding knowledge in Python and the major concept itself.
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