Are you looking for more context and qualitative data on audience sentiment about your brand? How about insights that will help with enhancing brand recognition and reputation, or anticipating and preventing social media crises? Then you need to implement social media sentiment analysis.
Social media is interwoven into our lives, with 43% of internet users using social media when researching things to buy, and 52% of online brand discovery occurring in public social feeds. Being responsive to social media is the number one thing marketers can do to prompt consumers to buy products. Given this, are you making the most of your social media marketing by leveraging social media sentiment analysis?
Here’s what social media sentiment analysis involves, and what you need to know about using it for your business.
What is social media sentiment analysis? Social media sentiment analysis is one subset of social listening, and it becomes a powerful tool when used right. Essentially, it involves retrieving information about consumers’ opinions or perceptions about a brand, product, or service. Usually, these opinions are in the text, in the form of social media posts.
Social media sentiment analysis is about going beyond quantitative data points to translate your customers’ emotions and opinions into actionable data for your business. So while social listening is about monitoring conversations, with sentiment analysis it’s about adding an extra layer of analysis by identifying the context and the underlying feelings and emotions ?of your audience.
Brand mentions. One example of sentiment analysis is collecting your brand mentions on a given social media platform, aggregating them, and determining whether they’re negative, positive, or neutral. You can do the same for specific products or services.
Monitoring these manually isn’t practical, so it’s advisable to use a social listening tool to track and assign a sentiment (positive, negative, or neutral, for example) to each mention. These tools feature processes like algorithms, natural language processing, machine learning, text polarity classification, text analysis, and computational linguistics. They let you automate the analysis process at a click. Additionally, techniques and tools like web data integration help you identify, extract, capture, and analyse and apply the right type of social media sentiment data at scale.
Tracking keywords. For example, you can set up popular tools like Sprout to listen in on your priority networks and track keywords and phrases related to your brand. You then assign sentiments to certain phrases. For example, terms like “best,” “love,” “amazing,” and “enjoy” in the same sentence as your product or brand reflect a positive sentiment. For negative sentiments, applicable terms might include “worst,” “ugh,” “avoid,” or “hate.”
As you can see, this type of analysis transcends data points like the number of likes on a post or upvotes on a product picture. It lets you capture contextual, qualitative data about your customers’ feelings and emotions about your products, services, and brand. You can then go deeper by manually reviewing the specific conversations for further insights.
Contextualised insights. In turn, these contextualised insights give you specific strategies for fine-tuning your marketing, product development, and customer service. A very basic example is receiving a comment on your Instagram page that reads, “I found it easy to order from your site and love the product.”
The algorithms would scan the words and assign a sentiment value. You’ve assigned a positive sentiment value to the words “easy” and “love” so the algorithm decides this comment is positive overall. You can even assign levels of intensity to negative and positive sentiments. For example, “love,” “fantastic,” and “incredible” could be associated with a higher level of intensity than “enjoy,” and “great.”
This is a basic example, but the latest social media sentiment analysis tools can accurately assign sentiment values to complex messages. Additionally, you can be alerted when something’s negative so you can respond in a timely manner.
Not all exposure is good exposure, which is where sentiment analysis comes in. Social media sentiment analysis is essential to any social listening strategy because it gives you the context for what’s being said about your brand, product, or service – at scale, through automation.
You can discover your audience’s feelings and emotions and how intense these are. This, in turn, lets you assess your brand health, deal with negativity before a crisis arises, and how your products/services are perceived relative to the competition.
If you want to improve your relationship with your customers, sentiment analysis gives you a great basis for better-targeted engagement since it gives you specific insights to producing messages that resonate and engage.
