Using Sentiment in Social Media Monitoring
If you have looked at social media monitoring platforms to help you better understand what consumers are saying about your brand on the social web, sentiment has probably come up on more than one occasion. In this post, we look at what sentiment means from a business perspective and the nuances of sentiment accuracy.
The Value of a Sentiment Score
A sentiment score can be an extremely useful in evaluating a large data set of social brand mentions. Sentiment scores can give users a straightforward way to segment and filter content based on positive or negative commentary, allowing them to isolate the themes or issues driving that sentiment. It also allows for dynamic and illustrative reporting of trends and market reactions, or situations like product recalls.
Each of these uses can help provide great insight into social data and can help propel a brand forward. It is important to remember, however, that while powerful and accurate scientific methods can be applied to analyzing what people are expressing online, understanding the context, sarcasm, intention, and wit used in human communications can require as much art as science. Keeping this in mind will prepare you to understand both the power and limitations of sentiment analysis.
The Nuances of Sentiment
One of the challenges of understanding and applying a sentiment analysis solution in a business setting is that sentiment is not a one-dimensional result with a universally agreed upon set of criteria. This is particularly true of social media content, and it means that evaluating the performance and accuracy of any solution is complex.
Removing the automated functionality and relying on human scorers wouldn’t solve the problem. According to Lexalytics, human analysts agree on sentiment scoring on average only 80 percent of the time. This means that a sentiment score will be wrong 1 out of every 5 times, even with strict judging by individuals who do their best to take context, subject matter, relevance, humor, and sarcasm into consideration. Subjectivity, variances in interpretation, and the context of what is being expressed create challenges for both humans and machines.
So where does that leave businesses that depend on the accuracy of analytics for critical business decisions, or early detection of negative incidents? Significantly better off than they were before. Sentiment analysis is still valuable, particularly when used alongside other indicators such as volume change and frequency analysis. It is important, however, to have realistic expectations of what is possible and an understanding of the limitations that exist.
How do you use sentiment in your social media program?
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