The usage of artificial intelligence (AI) versus human intelligence has long been debated, and it’s been established that these two make a mean team — especially when it comes to media analysis. And this couldn’t apply more to analysts who work with NLP.
This technology automatically analyses the sentiment of content, making the job of analysing the client's media coverage that much easier. But as we said, it does so much more than ease the blow of the tasks that come along with being an analyst.
So, before we jump straight into the helping hand known as NLP, it’s important to understand exactly what this technology does.
NLP is able to process and organise different words according to their word class. It does this through the recognition of the mechanics of language, such as sentence construction, helping it to group words into the correct categories.
For instance, it is able to categories the nouns, verbs and adjectives contained within a sentence, which allows it to identify the topic of the text. So if the noun ‘dog’ was identified, along with nouns like ‘hair’ and ‘nails’, the machine would pick up that this topic is about dog grooming.
Now that you know how it works, it’s time to take a look at what it can do for media analysis!
Here are three things NLP helps media analysts do in their day-to-day jobs:
1. It helps media analysts cipher through large quantities of data
AI technology does all the grunt work for analysts, helping them sort through their client’s data and media coverage in order to gather the information they need to compile easy-to-read, compact narrative reports.
At Focal Points, the media analysts essentially act as the storytellers of the data, while the technology does the heavy lifting. The analysts and techy work together in this way, as the data needs a human perspective to pull together trends and to gather and identity insights.
“During the analysis process, it is important to identify and separate what volume of the media coverage was produced from ‘owned’ or ‘client-specific’ content. This includes press releases or product specifications from journalists’ opinions or recommendations,” says April Parry, internal media analyst.
“We also use entity extraction in order to identify the adjectives that journalists use in order to gain this insight. This becomes an imperative part of brand benchmarking analysis where we identify the comparative hotspots,” she adds.
2. NLP helps media analysts generate statistically accurate reports
AI technologies like NLP assists brand media intelligence agencies such as Focal Points in producing analysis reports that are statistically correct.
This means that clients are able to gather all the insights they need — whether they’re looking to gather intel about their competitors via brand benchmarking reports or if they need to identify trends in the media with media trend summaries.
Once the data is collected, the media analysts are there to tell the story behind the data. “At Focal Points, we don’t offer ambiguous, meaningless statements like ‘Your clip count was up this month’ or ‘Your AVE was high’ — that’s not what we do. Insights, objectives, answers and explanations are the spaces we play in.”
Our analysts work closely with clients during the onboarding sessions, and beyond, to get all the information they need to turn the data extracted by our AI system into actual insights that can be utilised to improve and build on business strategies.
These sessions are intense, but necessary to give brands narrative reports that will help them better their business.
3. It allows analysts to identify sentiment more precisely
“At Focal Points, we use NLP and sentiment analysis to see whether an article portrays the client in a negative, positive or neutral light. For example, if an article accuses the client of corruption, it will assign the article with a negative sentiment,” says Parry.
The sentiment of a client’s coverage is important to gather, as it helps clients to see how other brands, publications and their consumers are perceiving them. Our AI systems are able to analyse the sentiment of media clips with a near-100% accuracy.
If the client receives negative coverage, they can then go back to the drawing board and revisit their strategy, essentially helping them to learn from the experience.
AI can help companies develop more intelligent products, optimise service and help internal business processes run without a hitch. Learn more about the Three ways AI can transform your business here.