In the digitized world, there are multiple platforms that contain a vast amount of unstructured data (text data) that is used by companies to enhance their business and understand their customer requirements with the products. News articles are one of the reasons for having a lot of information in text format. News articles are important for providing a lot of information about what happens around the world. In the development of the internet nowadays anyone can access the news media and articles just by clicking a button.
News articles often fall under multiple topic labels, for instance, if there is a transfer of a player from one club to another in football, this will be labeled under sports, likewise, there are many labels such as business, medical, commerce, banking, politics, and finance, etc. Identification of these classes is much needed to feed the reader based on relevance and their interest. Humans are able to classify and provide multiple labels to the articles, but can the machine do the classification? Can the machine do the automation?
With the help of Deep Learning and Natural Language Processing, the AISS does have such a system that categorizes the content of the text to the desired labels. We tagged it as AISS-TextProfiler.
AISS-TextProfiler does a process of assigning interested target classes to the natural language text with respect to its context. It can be used to categorize and organize pretty much any kind - from documents, medical studies, news articles, etc. all over the web.
TextProfiler is an inbuilt API from the AISS which is used for profiling the given text to the particular labels which are dynamically specified. It could be easily deployed without any training requirement.
Here is an example shown, how AISS-TextProfiler actually works. In this example we utilized a text from a news article and had given the interested label as entertainment; sports; politics; technology. AISS-TextProfiler profiles the text to the label "sports" with respect to the context of the text.
The unsupervised nature of AISS-TextProfiler allows us to include as many topics without any further modifications, from one to finite number of classes. So, while you are in need to including the new classes, even without any redeployment just pass the interested classes.
How AISS-TextProfiler can be used?
It could be used classify and analyse the intent of the news articles about the organizations or the reason for the post by the user. It may help the particular sector to detect the intent of the news or stories posted on the magazines.
It could be used to collate multiple topics from the news article and media like magazines etc. This may help the users to distinguish between different articles based on finance, sports, education, stock market etc. Another important aspect in topic classification is to recommend some articles to the users based on their previous reading habit.
It helps to find the aspects of the speaker or writer. For an instance, the news articles or online blogs about politics could be analyzed to classify the texts or content which is useful to understand the political ideology of the writer say, republican or democrat.
Magazines are mostly used press media which are published daily, weekly, monthly. There are generally two types of magazines namely special magazine and general magazine. Special magazine contains articles about cooking, filmfare, health-care, architecture etc. General magazine contains information about sports, business, film etc. AISS-TextProfiler able to classify these magazines based on their topics and content into special or general types.
Unlike the traditional way of identifying business objectives and then developing complete products around those business requirements, primary techs of AISS can be seamlessly integrated with each other in various sequences to create a complete solution which can achieve specific business requirements thereby greatly reducing associated time and costs.
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