With 2.13 billion monthly active Facebook users, 500 million tweets per day, and 8 million functional Instagram business profiles, social media users have the ability to interact with brands in real-time – something that was impossible 10 years ago.
This influx of virtual interaction in addition to the explosion of digital technologies has consumers feeling a sense of empowerment as they are able to directly and candidly communicate with brands.
As these users take to social media to publicly express their opinions, it is critical that each tweet, video, or hashtag is measured in context. Whether it’s a harrowing review or satisfied customer, understanding the social media sentiment behind each post has become a tool brand leaders and executives leverage as they implement new strategies and work to satisfy their target markets.
Systematically tracked by AI, sentiment scoring is categorized by attaching positive, negative or neutral sentiment to a social mention. But to accurately measure sentiment, leaders must understand that sentiment analysis platforms don’t always score correctly.
The problem with automatic social sentiment
AI and other sentiment analysis tools still have discrepancies in their accuracy in detecting sarcasm, understanding slang, or assuming the correct sentiment with each mention. A study conducted by Synthesios even stated, “No social media monitoring vendor would dare to pretend that technology can accurately (or even near accurately) assess sentiment on a specific topic.” This results in brands misinterpreting mentions that would otherwise have been impactful to their marketing and/or digital strategy.
And while a machine’s sole purpose is to remain consistent in its scoring, inaccurate sentiment data can lead to poorly made executive decisions. These decisions can contribute to failure in satisfying a company’s target audience and incorrectly inform PR and social teams as they predict market trends.
Through human eyes, manual sentiment scoring augments the process as experienced professionals are able to more accurately and holistically understand natural language. By combining both human curation and automatic tools, sentiment analysis becomes very powerful.
Why manual social media sentiment is important
Manual social media sentiment solves for the percentage of sentiment analysis that is mislabeled and ensures that companies comprehensively capture their target segment’s emotional perception of the brand. When done correctly, manual social sentiment scoring can:
Provide target segment insight
Support customer service and PR initiatives
Reinforce corporate and brand messaging
Measure brand perception
Add context to individual mentions
Contextualize data to bolster decision-making
Gauge brand health
Help brand leaders transition from crisis to strategy
Overall, AI and other sentiment analysis tools are great for data extraction and fast, consistent scoring. But as the human language is complex and emotions can be expressed in unique, often misunderstood ways, it is important to manually score social media sentiment to ensure you never misread a mention.
See how Turbine Labs enriches data through manual sentiment scoring and other forms of analysis to deliver brand-impacting insights that cultivate effective decision-making.