It’s difficult to pinpoint when Sentiment analysis first appeared as a concept. It’s long been considered valuable for companies to know how customers and the general public view their brand. Certainly by the early 2000s, as more and more businesses began to maintain an online presence, there was thought being put into the idea.
Via email and online sales, businesses started asking customers for feedback on the product and their experience interacting with the company. These types of surveys, alongside focus groups and generally bulky ways of researching public sentiment, were better than staying completely in the dark, but they were also expensive, labor intensive and very limiting.
This method relied heavily on those with an interest in the industry giving honest feedback. It also relied on customers, who are far more likely to participate in a survey or give any feedback at all if they’ve had a negative experience. Needless to say, businesses weren’t getting the full picture.
As online presence grew, it became clear that technology was lagging behind the need for good sentiment analysis. The explosion of social media moved the industry and the concept of sentiment analysis in the right direction, but it still didn’t go all the way.
Suddenly businesses could gauge public perception of their brands in new ways: the amount of likes on their social media accounts, the ratings, and reviews on their products and the ability to search for articles and blogs about them. While these all provided valuable new tools for companies, it was soon discovered that this too was lacking.
What does a social media “like” actually mean? Not much. Customers don’t necessarily like a brand that they “like” on social media — they may simply have an interesting in following them for updates. Maybe they even “liked” it by mistake and forgot it even existed. Online reviews have about as much reliability as a customer survey and it’s just too time-consuming to scan through enough blogs and articles to actually get a reasonable picture of public sentiment.
Then along came machine learning and advanced algorithms. The technology was finally catching up. In just the last few years, self-improving algorithms have taken over sentiment analysis operations, automatically scanning the internet and supplying valuable data on the overall positivity or negativity of public sentiment. And this doesn’t just happen for overall brand perception. The technology has finally come far enough that a business can gather sentiment analysis on literally any aspect of its operations. How about customer service, website UI, or the cleanliness of a specific room in a specific branch of the store.
Now blockchain technology is taking sentiment analysis even further, by making these advanced solutions more accessible. Senno has mounted a sentiment analysis platform on the blockchain to reduce the costs of the technology both in terms of finances and technical requirements. Senno even makes sentiment analysis accessible to individuals who want to consult public opinion in their day-to-day lives. Businesses also benefit from Senno’s constantly evolving platform and a wide range of options as programmers are incentivized to add new interfaces and tools to the platform to fulfill specific needs.
Sentiment analysis is now at the forefront of technological horizons, taking the path of advanced AI and versatile customization and evolution. It’s taken some time to develop, but the future of sentiment analysis looks like a bright one.
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