Social media sentiment is the perceived positive or negative mood being portrayed in a social media post or engagement.
If you could track sentiment accurately, it would help you understand the person’s feelings behind the post. As a marketer, this would be very useful, but would also be useful for individuals.
Certainly, there is sentiment behind every post. But can it be measured with any certainty?
There are scholarly articles about social media sentiment and a good number of companies that are working on trying to measure sentiment, which you might also see described as sentiment analysis or opinion mining. By any name, this is the analysis of the feelings (i.e. attitudes, emotions and opinions) behind the words.
Most of the tools use natural language processing. Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. It allows you to talk to your phone or some device in your home or car. These devices can understand your words (usually), but can they read your emotions? When you ask for directions are you angry, tired, frustrated or in a wonderful mood? When someone responds to an offer posted in social media by a company by saying “This is crazy!” is that a good kind of crazy or an insanity crazy?
In social media, this is analysis that goes beyond Likes, Shares or Comments. Did people respond to the original post in a positive, negative, sarcastic, humorous or biased way? A human reader may be able to discern that, but can a tool do the same thing for hundreds, thousands or millions of posts?
The complexity of emotional responses makes this analysis difficult. You have heard a lot lately about sites like Twitter and Facebook being told that they need to better monitor hate speech in their networks. How do you do that? Rely on users to report it? Have other humans monitor it? That won’t work when every second, on average, around 6,000 tweets are tweeted on Twitter, which corresponds to over 350,000 tweets per minute or 500 million tweets per day.
You need technology, but technology is famously not very good at reading human emotions.
There are some simple tools that some of you might already use for analysis. Hootsuite Insights and Facebook Insights and SocialMention are some of the easy and more common free(mium) tools for analysis, but they are lacking in the analysis of sentiment. Many businesses and individuals use Google Alerts as a simple way to monitor their name, brand, and to track “content marketing” with the result being emailed as they occur, daily or weekly.
We are still a good ways off from a time when some combination of NLP and AI can read the sentiments of social media posts accurately, but the desire and need for it only grows more critical as networks grow.