Why Text Analytics is essential in the ever changing Publishing industry
The publishing industry has changed dramatically. Mainstream newspapers and magazines have given way to desktop publishing and the Internet as economics have changed the game.
Let’s look at the main drivers behind this change.
More competition – Self-publishing has moved into mainstream online channels. The increase of entrants into the market means more choice and much of it is free.
The introduction of Apps – Apps create a more engaging and effective way to interact with an audience. The ever increasing ownership and usage of mobile devices mean that more readers can be reached.
Real-time social sharing – It can be argued that Facebook and Twitter provide the most up-to-date news channels. The sharing dimension can also be very appealing to readers who want to contribute to reporting the news, as opposed to passively receiving it.
Shift from mass to a niche market – Before the inception of the internet, successful newspapers and magazines appealed to the general public. Today, however, digital publishing has far lower production costs and a far greater reach to service niche markets.
According to Ofcom, use of the internet to consume news has increased for computers, laptops, tablets and mobiles since 2013, while TV has seen a small decrease from 78 to 75 percent. Use of any type of an online platform to consume news increased from 32 to 41 percent this year, and is now higher than the use of newspapers (40 percent) and radio (36 percent).
This shift in how we consume news has forced publishers to change their strategies in order to compete. More specifically, publishers understand that their content needs to be more relevant, richer, interactive, timely and discoverable.
Let’s say an editor hears about a bus crashing near a major school, close to a fire station. The editor wants to write about the story and they want to include historical information about the cause of bus crashes (e.g. time of day, time of year, equipment malfunction, driver error etc based on other bus crashes for the past 30 years) to give the story more depth and context. In most cases, a journalist would have tagged documents with dates and keywords. This is generally a manual process and therefore documents could easily be left untagged due to human error. Tags may be missed if different individuals are involved in the process. Some people may also not be as thorough as others. For instance, if somebody simply tags the document “bus crash”, it might be very difficult to find similar stories, much less analyze what happened in other relevant crashes.
Enter Text Analytics
By incorporating text analysis software, historical data can be culled for relevant concepts, entities, sentiments and relationships to produce a far richer tagging system. Information about the bus crash such as the type of bus involved, location, times, dates and causes could be extracted from the text. These entities would be kept as metadata about the articles and used when needed.
Text Analysis software can ‘understand’ the relationships between articles and provide suggestions to similar content. This benefits the editor as he or she can be far more productive as they navigate easily through a complete dataset. Research is, therefore, easier, a lot of time is saved and the end product is often richer, as the editor can reference similar events and give more depth and context to their article.
Richer, more relevant content can improve user engagement, meaning more page views by a narrower market, which can increase the potential for generating advertising revenue. A consumer that is more engaged with their content is far more likely to subscribe to niche newsletters, which can allow publishers to develop these relationships further and upsell their service to their consumers.
Text analytics is essential in the publishing industry because it saves time when gathering data, allows you to produce richer content to attract more readers in narrower markets, where consumers are often more loyal.