16th May 2022
Google’s Natural Language API is a cloud-hosted interface that anyone can utilise. Gifting us humans the ability to analyse huge amounts of data in seconds, this pre-programmed software might just be the marketing superstar you’ve been dreaming of.
- What is the Google Natural Language API?
- Use cases of the Google Natural Language API
- Features of the Google Natural Language API
Computers have changed the world, right? From flying planes to putting people on the moon, the last century would’ve looked very different without them. For some years, through a nifty creation known as natural language processing, they’ve been reading and evaluating text too!
What is the Google Natural Language API?
Natural language processing (NLP) gives computers the ability to read and analyse text in a way that’s closer to human comprehension. More advanced NLP can also hear and interpret speech. So yep, your Amazon Alexa isn’t actually your friend – it’s just a robot that can understand you (some of the time).
Google’s Cloud Natural Language API is an interface of NLP models, with each pre-programmed to analyse a specific task. It’s been designed with users in mind, so very little training is required to get started. Being stored in the cloud, there’s also no need to purchase any additional software, so it’s a space-saver to boot. The following NLP models are included:
- Syntax analysis
- Sentiment analysis
- Entity analysis
- Entity sentiment analysis
- Text classification
Use cases of the Google Natural Language API
Use cases of the Google Natural Language processing API are very much user-led and dependent on individual and industry needs. To offer some examples, it can be employed as a filtering tool to find specific text mentions within a massive document. Those monitoring their brand’s social media might use this feature to remove any posts that contain vulgar or offensive language, or HR professionals could employ it when searching for specific experience within a sky-scraping pile of CVs.
In addition, Google’s Natural Language API can pick up on emotive language to review articles for tone or judge whether a post has been machine-generated rather than crafted by a human hand. We’ll look at the features in greater detail below.
Features of the Google’s Natural Language API
What does the cloud Natural Language API do? Well, it’s been pre-trained to perform specific tasks relating to the analysis of text. As we’ll see, all functions have their uses, and when combined, users are able to extract crucial data in record time.
Just as a digital marketing campaign workflow helps you organise tasks and save time and money, using Google’s Natural Language API can accelerate your response times and allow you to work on data the moment you collect it – rather than waiting weeks or months for a human to analyse it.
It’s official – no matter how efficient Data Doreen the Developer is, she simply can’t skim-read like a computer and deliver on-the-spot analysis in seconds.
1. Syntax analysis
Syntax analysis looks at the structure of the language itself, as opposed to its direct meaning. It can decipher word type (verb, adjective, noun etc.), tense and grammar. Running syntax analysis can tell you if an article has been structured correctly within the grammatical rules of that language. It can also check sentences and paragraphs for logic.
2. Sentiment analysis
Emotive analysis is useful for brands on a number of levels. It might be used to check all brand sentiments are consistent and in keeping with company guidelines. It could also be used for reviewing staff or customer feedback and separating it into the good, the bad, and the neutral.
This is potentially a huge boon when it comes to reviewing 1000s of responses or social media posts. As it can be set to work in real time, you can keep track of any growing incidents of customer unrest and deal with them before they grow too large.
3. Entity analysis
Entity analysis enables you to search the text for specific names or places. Google uses an entity analysis tool when scouring the text for keywords and phrases in its process of evaluating website pages. For businesses, it’s a valuable tool for analysing huge amounts of customer data found through feedback, surveys, emails or product reviews.
You can also use entity analysis to search for brand mentions on the likes of forums or news sites, and personalised product recommendations are often automated by brands through entity extractions of previous user behaviour. If James Bond was into marketing, he’d be using this tool almost as much as his pistol.
4. Entity sentiment analysis
Combining both entity-specific words or phrases – and sentiment – the author’s mood – gives a supercharged analysis of a piece of text. Entity sentiment analysis looks for the emotion found within a given article. It then attempts to link this emotion to your identified entity.
This can be invaluable when ascertaining customer responses to purchased products or positive/negative reactions to your brand. Proactive reactions to feedback are a great way of building and maintaining relationships with customers.
5. Text classification
Text classification analyses the content of an article and fits it into a pre-determined category. For example, if you’ve written an article about Rottweilers, foxes and hyenas, text classification could return a category of ‘dogs’.
It’s a useful tool when trying to analyse huge amounts of data into sections that are relevant to what you’re searching for. You could even use it for taking a machine-led approach to your own content – analysing it to see if your intended subject is clear enough.
In a world where digital marketers spend an enormous amount of time attempting to sift through reams of data, Google’s Natural Language API can help bridge the gap between human and machine learning. Those working in digital marketing will find it ultra-useful when it comes to understanding how search engine bots perform. As a time and money saver, it can help you react to customer comments almost as they happen.
Although the interface is still relatively young, adding an NLP to your own suite of analytical tools can offer you a great brand health check and, over time, will make invaluable, data-led contributions to future business strategies.
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