Social media has revolutionized how humans communicate. With more than 7 in 10 people now using SM to share information and opinions, modern brand image management must include a method to monitor the opinions of SM users.
Perhaps the most cutting-edge topic in brand image is how rapidly sentimental analysis is gaining competence in natural language processing. NLP has advanced to the point where the unique structure and language used in a social media context can be parsed into “word vectors” with extremely complex interrelationships to evaluate nuances that traditionally only humans could distinguish.
What this means for brands is that AI can determine the attitude and feelings of SM users through their posts and replies. Sentiment analysis uses neural networks and machine learning to gauge if a post is positive or negative towards a brand – a surprisingly difficult task.
Why is it difficult?
Natural language is tricky. It’s filled with slang, sarcasm, and double meanings – and word meaning can vary enormously based on context. For instance, if site user posts, “I really love this brand, they are the best!” that might mean one thing if posted with a picture of them holding a branded object and smiling, and something else entirely if the picture shows them striking the object with a baseball bat.
Another huge problem is the intentional keyboard spam and misspellings. When evaluating the meaning, humans immediately either discard or understand it as a modifier on the more standard definition of the word. An example of this might be, “I reallyyyyyy like these potato chips!”
How is it done?
Neural networks are built using humans to train them with as many real-world examples as possible gradually. They use datasets of SM posts that number in the millions to build an understanding of natural language subtleties.
Each word vector is given a set of coordinate points in a specific number of dimensions. By evaluating the distances between words, it can determine the mood or feeling of the post. The more dimensions added to each word’s coordinates, the more accurate the results will be. The number of dimensions determines the geometry of the dataset in the n-dimensional mathematical model.
How does this impact brand image?
Broadly speaking, the analysis models look at three factors:
First, is the comment positive, neutral, or negative? Second, does it express emotions, such as happiness or disappointment? Third, how intense is the comment? Do they like it or love it? Plus, these factors can be broken down even further, but the more fine-grained your analysis, the more expensive it will be.
If large numbers of SM users are posting negatively about your brand, you need to be aware of that as soon as possible to mitigate the damage. A negative post by (or boosted by) an influencer can go viral in minutes, so it’s essential to gauge brand image before the number of mentions starts bumping them up into viral territory.
SM is also one of the best places to gauge the popularity and problems with your products. Sentiment analysis can quickly pick up on trends among huge datasets that would take humans months or years to parse.
Is there much data available for brand image?
The entire point of SM is to collect huge volumes of data about consumers. The problem is never the lack of data – it’s how to find a particular tree in a massive forest. Sentimental analysis cuts through the clutter to create a detailed report of brand opinion and spot opportunities for improvement and trends in what consumers most want from your business.
Sentimental analysis helps you find your ideal brand tone that matches your customer base on social media. If you’re ignoring your reputation on SM, you probably cost yourself a lot of revenue.