Data is growing day by day, at a speed and power never seen before, making it important to have easier ways of finding information. With this range of knowledge available, it is essential to find the right information at the right time. Text classification helps us to do exactly that.
Remember how easy it used to be finding books in libraries because they were classified in the right sections. AI and Text Classification are essentially doing the same thing with content.
Text classification, also known as text categorization, is the process of labeling natural language texts into correct categories from a predefined set. This process of classifying content helps users to easily navigate and look for content within websites and applications. In a layman’s term, text classification helps to simplify information. In this blog, we will try to understand Text classification, its significance, and current applications.
What is text classification?
Text classification is the process of extraction of generic tags from unstructured texts, by using Natural Language Processing (NLP). These tags come from a pre-defined set of categories. Text classification makes content classification automated and easier, making it highly useful in industries such as e-commerce, news agencies, healthcare, and content curating. Automating content classification in websites helps standardize it, creating a better user experience.
For example, say news articles need to be categorized into two groups, politics and sports. We will define categories with words such as cricket, football, and Pele for sports, and Prime Minister, Parliament, and elections for politics. The text classification system here will count the news related words in each article. In case the sports-related words are more, the article will be classified as sports news and vice versa.
Text classification can be used wherever there is a large amount of text data, from search engines to social media platforms. It can classify short texts as well as organize large documents. Since it analyzes and extracts information from texts, it’s highly useful in industries like Marketing. Analyzing conversations with customers is important for marketers as they need to use personalization to drive better results.
Text classification helps them understand communication with their customers on social media platforms. It improves brand reputation. In case of an emergency or panic conversation taking place on social media, a faster response system can be implemented. Here are some more current applications of text classification.
Use Cases and applications of text classification
Media & Publishing
The media and publishing industry is leveraging the power of text classification through content categorization. The technology helps in categorizing large amounts of text, image, audio, video content, into predefined topics.
Content categorization helps in identifying related content on websites and grouping them into the right categories. This improves search accuracy and helps users to make sense of various content. Users can navigate and browse through large amounts of content, search results, and websites, with the help of categorization. Irrelevant and non-interesting content can easily be ignored by the user and they can focus on what is important.
Ecommerce businesses need to offer customers a wide variety of products and services. Text classification helps in product categorization by tagging them using categories. Product categorization helps with improved browsing and identifying related products. Consumers are therefore able to find the products easily. Since the product portfolio keeps growing, it becomes impossible to do Categorization manually. Text classification helps in product categorization by grouping the products into predefined topics. The process is automated, and products are categorized as and when they are added.
Customer service teams are required to spend a large amount of time going through chats and queries, to understand what the text is trying to convey, and resolve the issues.
Text classification makes this task easier through topic modeling. It structures and organizes data depending upon the topic. Customer chats and emails can be categorized easily, enabling customer service representatives to attend to a bulk of customer queries in a short time. It also helps with daily activities like spam filtering.
Text classification identifies customer intention. The text classification model can be trained to identify words like ‘Unsubscribe’ or ‘Not interested’ in their outbound sales emails.
Text classification is powering new-age learning techniques in the form of test assessments and ratings. This technology enables teachers to assess student learning through tests, and rate them according to the results. It helps educators make more informed decisions and encourages an assessment-centered learning system for students.
Text classification helps bring simplification to content. It is a powerful tool to extract value from unstructured data. Thousands of texts can be analyzed to get information such as sentiment, language, topics, etc. And it is amazing to see how marketers, product managers, healthcare professionals, and academicians can equally make use of this technology.
Check out the various Text Classification solutions that can be built using Skyl.ai Platform here.