It is the process of determining if a piece of writing is positive, negative or neutral. This system for text analysis combines natural language processing and machine learning techniques to assign weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase. Sentiment analysis and artificial intelligence services enable data analysts to analyze public opinions, conduct in-depth market research, evaluate brand reputation, and enhance customer experiences. Moreover, businesses may integrate third-party sentiment analysis APIs into their database management systems for different platforms to extract customer insights.
Text documents follows the following process:
There can be many methods and algorithms to implement this systems, which can be classified as:
In today’s day and age, brands of all shapes and sizes have meaningful interactions with customers, leads, and even competition on social networks like Facebook, Twitter, and Instagram. Most marketing departments are already tuned into online mentions as far as volume –they measure more chatter as more brand awareness. Nowadays, however, we can take a step deeper. By sentiment analysis on social media, we can get incredible insights into the quality of conversation that’s happening around a brand.
Not only do brands have a wealth of information available on social media, but they also can look more broadly across the internet to see how people are talking about them online. Instead of focusing on specific social media platforms such as Facebook and Twitter, we can target mentions in places like news, blogs, and forums –again, looking at not just the volume of mentions, but also the quality of those mentions.
Social media & brand monitoring offer us immediate, unfiltered, invaluable information on customer sentiment. In parallel vein run two other troves of insight –surveys and customer support interactions. Teams often look at their Net Promoter Score (NPS), but we can also apply these analyses to any type of survey or communication channel that yields textual customer feedback.
In numerical survey data it is easily aggregated and assessed, but we want that same ease with the “why” answers as well. Regular NPS score simply gives a number, without the additional context of what it’s about and why the score landed there. However, AI-powered sentiment analysis provides an automated description of the text including the “what” and “why”.
We all know that stellar customer experiences = more probable returning customers. Particularly in recent years, there’s been a lot of talk (rightfully so) around customer experience and customer journeys. Leading companies have begun to realize that oftentimes how they deliver is just as (if not more) important as what they deliver. For these days, more than ever, customers expect their experience with companies to be immediate, intuitive, personal, and hassle-free. In fact, research shows that will switch to a competitor after just one negative interaction.
To sum up, this could imply that a more personal, engaging take on social media elicits more positive responses and higher customer satisfaction.