Have you been wondering what Natural Language Processing (NLP) is and why so many companies are using it? We created this guide to everything NLP to walk you through what it is, how it works, where you should be using it and some tips to get you started! It’s a primer you’ll want to bookmark for later reference as you sort out which tools to have in your marketing stack – and to help you describe NLP’s importance to key stakeholders in your company.
What is Natural Language Processing?
The history of Natural Language Processing began in the early 1900s, with a Swiss linguistics professor named Ferdinand de Saussure. “From 1906 to 1911, Professor Saussure offered three courses at the University of Geneva on ‘Language as a Science.’ He developed an approach describing languages as ‘systems,’ where within the language, a sound represents a concept – a concept that shifts meaning as the context changes. . . . [And] meaning is created inside language, in the relations and differences between its parts.”
Following that, in 1950, “Alan Turing wrote a paper describing a test for a ‘thinking’ machine [that could] imitate a human so completely there were no noticeable differences” and “helped inspire the idea of Artificial Intelligence (AI), Natural Language Processing (NLP), and the evolution of computers.”
Having come through many advancements and iterations, today at NetBase Quid, Natural Language Processing (NLP) is the technology that we use to decipher and perform deep parsing and grammatical analysis of data. We process, normalize, and organize conversations so you can analyze them.
And it gets smarter all the time, which is something the world is taking note of. As we can see on March 23, 2021, there was a spike in Impressions around the already substantial NLP conversation online as Google shared an NLP update. It’s a key part of AI, after all and the world is increasingly fueled by artificial intelligence:
NLP processes every post that customers download to extract insights, such as positive or negative sentiment, emotions, behaviors, people names, and other data, before NetBase Quid stores the post in its index.
And from there, it gets really interesting.
How does Natural Language Processing Work?
Most Natural Language Processing technologies focus only on sentence tone, but our NLP engine surfaces and analyzes sentiment for every subject in the sentence. As an example, consider the following sentences about the iPhone:
Even though both sentences include the word “good,” it is clear to a human reader that the first sentence expresses a negative opinion about the iPhone while the other expresses a positive opinion. Natural Language Processing automatically understands the difference between sentences like these and uses it to accurately classify and extract insights from conversations.
In addition, Natural Language Processing:
- Enables NetBase Quid to read and interpret the meaning of consumers’ social media opinions with a high level of accuracy.
- Analyzes not only the most basic of sentence structures, but also data based on all of the variations that may occur in over forty different languages.
- Distinguishes between and captures misspellings, sarcasm, and emojis to extract information and sentiment. For example, NetBase Quid processes:
- Urban words or “slanguage,” for example, “My new phone is sick!”
- Alternative spellings, for example, “luv,” “kewl”, or “gr8”
- Abbreviations, for example, “IMHO,” “ttyl”
- Common misspellings, for example, “teh/the”
And the most powerful differentiator available when it comes to data analytics is accuracy. It’s something companies must have, but it’s also something most don’t realize is lacking right away, because they aren’t able to see what their tool’s sentiment is based on.
For example, if we wanted to see what was driving the reported emotion on the word “pleasant” below, found in a our sentiment driver widget – we should be able to do so within a few clicks. In NetBase Quid, we can filter on that precise word and see the posts supporting that result and determine whether or not the tool has accurately classified it.
And what does NLP extract specifically to make these determinations though, as these posts can get tricky, obviously! Let’s find out . . .
What does Natural Language Processing Extract From Posts?
Natural Language Processing provides you with a deep understanding of customer preferences, passions, and behaviors. This insight is key to helping brands drive customer experience initiatives and also to make smarter, faster business decisions. And it starts with insights.
An insight is a piece of data that reflects what authors are saying about a brand. We extract insights by analyzing the full post in which a mention appears. A mention often contains multiple insights. An insight can be:
- An attribute (such as “fries” or “chicken nuggets”)
- A behavior, such as “eat,” “crave,” or “shop”
- An emotion, such as “love,” “hate,” “adore,” or “loathe”
- Opinions, such as likes and dislikes
- Brand names
- People names
- Product names
- Company names
Sentiment reflects how authors feel about a brand overall or about specific insights. NetBase Quid extracts sentiment by analyzing sentiment-based insights (such as likes and dislikes, positive and negative behaviors) and how they relate to the brand.
- Sentiment can be positive, negative, mixed (contains both positive and negative sentiment).
- NLP technology looks for positive or negative sentiment towards the objects in a sound bite. If none is found, the sound bite defaults to neutral.
- When mentions are assessed by humans, typically only one-third of a brand’s mentions contain sentiment towards that brand.
And then metrics provide counts, scores, and other values that enable organizations to analyze social media content. Here are just a few of the metrics that a company could track for social web analysis:
- Mentions: The total number of sound bites that mention a brand on the social web.
- Impressions: The estimated number of people who might have viewed an author’s posts.
- Net sentiment: A score that expresses the ratio of positive to negative sentiment about a brand, which we measure on a scale from -100 to 100.
- Passion Intensity: A score that expresses the ratio of strong emotions (such as “love” or “hate”) to all emotions expressed about a brand.
Where Can Companies Use Natural Language Processing?
Companies can use Natural Language Processing as a key element in every strategic initiative – and they should.
As co-founders of 113 Industries, Razi Imam and Anupam Singh shared – robust social data helps them discover behavioral conversations and consumer preferences within specific categories. More specifically, it helps them distill social conversation and transform them into new product insights that customers truly want.
Natural Language Processing reveals the reasons behind consumers’ buying, using, rejecting products and repeat buying practices. More importantly, it uncovers compensating behaviors. These are hacks that a consumer uses when a product doesn’t do exactly that they need it to do. And these unarticulated needs are product development gold to businesses.
For example, we see IKEA has paid such close attention to these ‘hackers’ that there’s an entire movement of IKEA fanatics that share their #IKEAhacks online. As the unofficial IKEAhacker, Jules Yap found that “consumers are organically trading secrets in regards to IKEA set-up, [and] ideas that could be incorporated into the brand’s marketing.”
And she’s not paid by IKEA to do this either (she speaks to this fact in her “about”) – and that’s how much fan love there is out there for some brands. Imagine tapping into that for ideas?
How NetBase Quid Helps Businesses with Natural Language Processing
NetBase Quid makes Natural Language Processing simple to work with. With so much happening on the backend, and such a high level of easily confirmed accuracy, NetBase Quid users are able to focus on the output instead of worrying over what’s driving it.
And when we get to the user view of the insight, it’s super easy to navigate, keep track of, export and report out via dashboards.
NLP isn’t magic, but its results can appear that way to brands. Language can be ambiguous or refer to different things in different contexts, and a fine-tuned NLP capability can employ deep parsing criteria to decode and transform it into useful, immediately actionable insight. And it forms the foundation of all consumer intelligence work, spanning a variety of use cases, including:
- Brand Health & Perception
- Campaign Strategy
- Product Innovation & Launch
- Trend Analytics
- Mergers & Acquisitions
- Competitive Intelligence
- Crisis Management
- Voice of the Customer
- Influencer & KOL Marketing
- Technology Scouting
The underlying sentiment of insight that informs decision-making in each area is key to uncovering valuable intel vs that which is just noise – or sound and fury that really signifies nothing. If a brand misses a series of sarcastic posts meant to cause harm or sway the market in some way, they can have a huge problem on their hands. Conversely, if a brand hyper-focuses on miscategorized insight that distracts them from an actual issue, they will appear out-of-touch (at the least) and certainly tone deaf. And that’s never a good look in any category.
Consumers and investors are watching – closely. And your next move must be one informed by the best Natural Language Processing has to offer. Reach out for a demo and we’ll show you how that looks!