Natural Language Processing (NLP) possesses a massive influence on the media and communication industry. The ability to track people’s choice, filter irrelevant information, speed, and accuracy makes this technology standing apart in the industry.
In this write-up, we will understand the role of NLP in the media industry, its impact, and how it will help to clear out the issues which are hampering the overall growth.
But first, let's have a look at the basics.
What is Natural Language Processing (NLP)?
Natural Language Processing or NLP is an automatic manipulation of natural language. It is a branch of Artificial Intelligence (AI) that allows systems to instantly recognize, manipulate and correctly interpret the way human beings communicate - majorly in the form of speech or text.
With the advancement of technologies, even machines can easily decipher the human language and perform the tasks accordingly.
Earlier, the “punch cards” were used by programmers to communicate with machines. Presently, the place has been captured by Siri, Alexa, and other machines, which can fluently communicate.
NLP in the Media & Communication Industry
The implementation of Natural Language Processing in the media industry has already started across the globe. As we all know, fake news, irrelevant content & comments go hand in hand with social media trolls.
From the user's point of view, almost everyone has grappled with these types of issues. It is an arduous task for the human mind to properly maintain track of everything. Therefore, to get rid of these problems, NLP plays a substantial role.
This will not only help to eliminate the above-mentioned problems but will also open the door for the overall development of the industry. According to the predefined criteria or specific guidelines, computer intelligence will allow automated searching for crucial information, parsing relevant news, and analyzing the news.
We can see the successful implementation of NLP by an American news agency called “Associated Press (AP)" in 2015. According to a report, as many as 3,000 articles per 15 minutes were generated. In 2016, it was around 2000 posts per second.
Apart from AP, other media agencies like The New York Times, The Guardian, Forbes, BBC have also implemented this technology.
Impact of NLP in the News Industry
Any advanced technology has its advantages and disadvantages. It is up to humans to what extent they want to incorporate the technology.
When we talk about the use of NLP in the news industry, it is not only about understanding the text or the speech. It is crucial to develop the algorithms which enable computers to perform the following actions:
1. Predetermine the textual content
2. Summarizing the overall information
3. Analyzing suitable information
4. Filtering the news as per the criteria
All the above steps can only be possible through macro-understanding and micro-understanding of the textual content.
When we talk about integrating NLP in the journalism/media industry, there are immense possibilities where we can assimilate. All the momentous happenings, events, and other significant information are crucial in our daily lives top 6 familiar examples of NLP
It is challenging for humans to remain 100 percent correct and accurate every time. This arises the need for machines that can think, understand and interpret just like humans do.
For a journalist, NLP-enabled robots can carry out a significant role in developing the overall industry standards. In order to enhance productivity, accuracy, and speed, a journalist can dedicate the research work to a robot and manage the other aspects of the news vertical.
These robots can scan relevant and authentic information from the internet and create a news article or any other news piece. NLP robots can play a crucial role in delivering the content, which requires figures, statistics, and other technical details.
As we mentioned earlier, every technology enjoys its advantages and disadvantages. Implementing NLP in journalism also has few challenges
Machines are capable of handling multiple assigned tasks at any given point in time as compared to humans. They can perform the work faster and effectively, but there are certain aspects in which machines cannot match the intelligence of the human brain.
Other factors like freedom of speech, social and ethical issues, public sentiments cannot be handled by the machines.
However, when the algorithms of the machines are fed with quality data, it will impact the overall result, and machines can perform the majority of tasks equally.
To train any kind of data, one needs NLP annotation services. It helps machine learning acquire the applicable words from the sentence and make it understandable for AI words.
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