Text classification and neural networks

With more and more content (text) being added to the World Wide Web day in day out, text indexing and searching has become more complex. This has motivated developers to come up with ways to classify text to make it easier for internet users to find documents on the web. One way that has proven quite effective in document classification is the use of machine learning and artificial intelligence. This post aims to explore the concept of text classification and neural networks and how its implementation has simplified the technique of content searching.

What is text classification?

According to our neural networks experts, text classification is the process  of creating predefined categories in free text. The word ‘text’ refers to documents, emails, Instagram feeds, tweets, Facebook feeds, and basically any content that contains text. We use text in real world every day and one application that you may be enjoying on a daily basis is the spam detection that is incorporated into emails. Some people may also like the news feed  which is always organized according to the topics that one searches frequently. For businesses, the sentiment analysis achieved from text classification, is an important aspect for understanding how customers feel about their products or/and services. To learn more about how text classification is used in the real world, connect with our text classification and neural networks assignment help experts.

How neural networks are used for text classification

Classifying text using neural networks sounds pretty easy but it can be an extremely daunting task. You see, neural networks are like mystery magic boxes; you do not know exactly what is happening inside or how the results of a certain operation were achieved. And here is what makes text classification with neural networks specifically difficult; before you even use the mystery magic box (computer model), you will need to actually create it. You need to know what to put in it, the number of hidden layers it will have, the kind of heuristics you will be putting in, as well as the kind of output to expect. Putting all these things together is not an easy feat especially if you are just getting started with Natural Language Processing (NLP). NLP is a sub-discipline of artificial intelligence and computer science concerned with how computer systems interact with the human (natural) language.

Once you have put all these things together, our text classification homework help experts argue that you need to train your model too. But how do you know whether the model has been trained properly or whether the data and text you put in is correct? You will need to create an algorithm that does all these sorts of things. However, to create an algorithm that classifies this data, you will need to build a data structure that contains a certain set of words per classification. You will then need to train the algorithm to make sure it understands each type of text in the classification.

After creating a bunch of classifications, you can leverage that to create and classify new sentences. The process of preparing new sentences is as follows:

  1. Tokenize the sentence
  2. Filter out stop words
  3. Stem the words

Once you have prepared the sentences, go through the words in each sentence and compare it with the text in your different classifications and create relationships that will help the user find this text after he/she enters the corresponding sentences in a search engine.

Classifying text this way can be a cumbersome task for the average human being. That’s why computer systems are trained through artificial intelligence to perform this task. To understand this topic further, connect with our neural network experts.