The process of labeling documents into categories based on the type of the content is known as document classification. It can also be defined as the process of assigning one or more classes or categories to a document (depending on the type of content) to make it easy to sort and manage images, texts, and videos. Document classification can be done using artificial intelligence, machine learning, and python. Types of Document Classification This classification can be done in two ways: manually or automatically. The former gives humans full authority over the classification. In this type of document classifications, one can decide which category to use to put a particular type of content. Manual classification is not recommended to use for handling and processing a large volume of documents. The second type of classification, i.e., automatic document classification is fueled by NLP or Natural Language Processing. It can efficiently and effortlessly process large data and provide accurate outcomes. Is document classification different from text classification? Both these terms might sound similar to you, while the truth is that they are not the same completely. Text classification deals with categorizing texts available in a document. The techniques used for this include topic labeling, sentimental analysis, intent detection, and more. You can perform text classification on document-level, sentence-level, paragraph level, and sub-sentence level. Depending on the requirement, one from these two methods is used by the developers of a machine learning company. Automatic Document Classification- How does it work? Here, in this article, we are providing the complete working of automatic document classification because this type of classification is widely used by the developers. Manual classification is slow and monotonous. With automatic document classification, a document can be analyzed, managed, and sorted to provide valuable outcomes. Powered by natural language processing, the process of ADC helps in assigning categories to various documents including articles, survey responses, and more. With automatic document classification, you can vouch for delivering accurate outcomes for every document. ADC follows for the categorization: three different approaches Depending on different rules based on phonology, morphology, syntax, lexis, and semantics, and more, this approach trains the machine learning model. Using the created pattern, the model then tags the texts automatically. Based on rules - This approach needs a user to define the tags at first and then the ML-based models and algorithms will automatically classify it on its own. Supervised - In this approach, the classifiers analyze documents with the same words or sentences and put them together in one group. This doesn’t require any prior training. Unsupervised - How Document Classification can improve business process? The advent of technologies like artificial intelligence, machine learning, and others have made it easy for the businesses to get huge data from the web. This data can be used to get valuable customers’ insights. Yes, they can know who their audiences are, what type of products they are looking for, how much they are willing to spend for a product or service, and more. This data can help them to decide marketing strategies and take futuristic decisions for the growth and success of the business. Now, the problem is that the data collected from the web is unstructured. In other words, the articles, support tickets, survey responses, feedback, and other data containing the informational insights are not easy to understand. Moreover, you cannot read, process, and categorize this vast amount of data. Here is when , mainly automatic document classification comes into the picture. ADC can help both the startups and enterprises to get the information from this data by categorizing them based on the trained algorithms. It can help in: document classification ADC can classify emails for better management. It can filter spam emails or the one containing unnecessary messages by analyzing the words included in it. Managing emails - ADC can categorize documents based on the positive, negative, neutral sentiments. Analyzing sentiments - Document classification can assist in recognizing the topic, genre, or the language of the text to deliver valuable information like age groups, gender, and interests of the users searching for a particular product or service. Finding users’ interests - With ADC, businesses can collect useful information by monitoring and tracking the data from social media platforms, product review websites, and other sources. Analytics - - Document classifications can help in triaging, i.e., to sort and prioritize documents in a particular order. Businesses or companies can use it to label documents and send them to a particular team in your organization. Sorting Steps in Document Classification Majorly, there are two main steps in classifying a document: 1. Prepare the datasets For preparing the datasets, it is imperative that it should include documents from all the categories you want to examine. This will help to train the ML model to distinguish between documents. 2. Algorithm or model training After you collect enough data required to train the ML or AI algorithm, the next thing to do is start training. For this, you can either use various open-source tools or create a classifier from the beginning. Both these cases require you to have the basic knowledge of machine learning and other technologies. Wrapping Up Documents and data available online can help businesses to get valuable information that can lead to the success and growth of their business. However, collecting documents such as articles, customer feedback, survey responses, etc. is not sufficient. One needs to analyze them to find out the information that can be fruitful for a business. Doing the same manually is not a viable option as it will be time-consuming, monotonous, and inaccurate. Here is when document classification can help you. With automatic document classification, you can categorize documents based on various factors.

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