It is said that close to 11 new users start using social media every second. That’s a goldmine of social insights, available at the fingertips of marketers. But even though it is widely acknowledged that such insights can help businesses craft a more informed and data-driven strategy to achieve their desired results, many companies fail to do so.
This is where social media sentiment analysis comes in – the ability to understand the context of users’ online conversations can give brands an edge over their competitors.
What is sentiment analysis?
Simply defined, sentiment analysis is the process of identifying users’ opinions expressed in text and categorizing them as positive, negative, or neutral.
You can run this process manually by reading through every message, comment, or review you received from your audience and labelling them accordingly – an extremely time-consuming process.
To speed things up, you can automate your sentiment analysis activities and have smart algorithms do the job for you. As a result, you’re able to analyze whole campaigns in a single click, compare their results, and even monitor their evolution over time.
On a basic level, automated sentiment analysis involves advanced technology, such as machine learning, natural language processing, and text polarity classification.
Thanks to all these processes, you can analyze the sentiment of any written text across the Internet.
Automated sentiment analysis on Social Media
Imagine you receive the following review on your Facebook page:
“I really enjoy how easy this product is to use and how it successfully helps my team complete their day-to-day tasks.”
Looking at this sentence, you can immediately tell the review is positive.
But how would computer algorithms determine its sentiment?
Firstly, they would examine each word of the review and establish whether the mood they’re conveying is positive, negative, or neutral. Then, they would measure how many words there are in each category and, based on this number, assign a sentiment type.
In the case of the mentioned review, there are two positively charged words – “enjoy” and “successfully” – which indicate that the entire sentence is positive.
This is a very basic example and you’ll likely receive more elaborate messages from your audience, but complexity is not an issue for the algorithms running social media sentiment analysis.
Today’s advanced sentiment analysis tools can break down roughly any written message and determine its sentiment in no-time. As such, you’re able to quickly learn the feelings and emotions behind your audience’s messages and respond appropriately.
Social media sentiment analysis vs. listening: what’s the difference?
Sentiment analysis differs from listening as follows:
In essence, sentiment analysis adds a critical layer of insight to social media listening. It complements the information you collect through listening and helps you build a more accurate picture of the ongoing online discussions around your business.
For example, here’s a tweet mentioning Socialbakers:
Now, let’s imagine we want to monitor conversations about Socialbakers happening on Twitter.
Using social media listening would allow us to discover this particular tweet and learn that the Twitter user @StickandGrow mentioned Socialbakers and our persona template tool in his post. In other words, listening would enable us to find out the number and names of users mentioning our brand.
Using social media sentiment analysis would help us understand the context of @StickandGrow’s post. We would be able to see that the user expressed a positive attitude in his message and learn that he liked our tool.
To sum up, sentiment analysis is an invaluable addition to listening, giving you the key to understand online conversations and brand perception: the context.
Benefits of sentiment analysis for brands
Here are five of the most important benefits of social media sentiment analysis your business simply can’t overlook:
Tracking your brand perception. Sentiment analysis is essential for monitoring your brand’s reputation across social media. It allows you to gauge users’ opinions about your business and understand what steps you need to take to enhance your brand’s image.
Adding depth to your performance analysis. Sometimes, raw KPIs values won’t tell you the whole story. Social media sentiment analysis adds context to your performance and helps you figure out why certain trends, such as spikes in engagement or follower growth, are happening.
Gaining competitive insights. You don’t have to limit yourself to only measuring feelings and emotions around your brand. Sentiment analysis works great for uncovering users’ opinions about your competitors too, which can help you make better tactical decisions and get ahead of the game.
Improving relationships with your audience. Knowing how your audiences feel about your business is important to building lasting customer relationships. It allows you to tailor your content and community management strategies and ensure your messaging resonates properly.
Spotting and preventing potential crises. Monitoring the emotions expressed by your audience can act as a crisis detector. By identifying an influx of negative tone messages early on, you’re able to spot the upcoming crisis and take measures to stop it before it spreads.
Social media sentiment analysis adds a whole new dimension to your performance insights. It enables you to uncover your audience’s emotions about your brand, which can shape your social media marketing strategy and make it much more effective.
Co-written by Marta Buryan, Content Marketing Specialist