Marketers are turning to sentiment analysis to assess the tone and sentiment expressed in comments, posts and conversations around their brand to determine whether they are positive, negative or neutral. This is a critical AI capability considering 44% of marketers, per The State of Social Media Report, use sentiment mining to understand customer feedback and improve how they respond to issues.
Analyzing sentiment in social chatter also helps brands spot early indications of negative sentiment and take proactive measures before a situation escalates.
For example, in Sprout, you can detect unusual spikes in doctor data brand mentions and monitor whether they are negative or positive. This enables you to actively monitor your reputation to ensure brand health. Similarly, sentiment analysis algorithms also tag incoming messages as positive and negative so your social customer care teams can prioritize them based on how critical they are.
Screenshot of Sprout's sentiment analysis feature that tracks the sentiment in your social listening data to track customer sentiment and emerging trends.
Creating connections and building community requires a lot of time and effort, both of which are limited for already strapped social marketing teams. AI can address this challenge by automating functions, simplifying workflows and increasing team transparency. However, there is apprehension from both social marketers as well as customers. While social teams worry about job displacement, 42% of consumers, per the 2023 Social Index, are apprehensive about brands using AI in social media interactions fearing reduced human interaction.