Using AI to monitor sentiment in social listening

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Modern AI-powered jordan business email list tools like Sprout go beyond basic sentiment analysis to detect specific emotions and sentiments in social posts and comments. This gives brands real-time information on how consumers feel about their campaigns, products or services.

Sprout’s sentiment analysis model applies aspect-clustering to identify and extract relevant details from social listening data that can span millions of data points in real-time. It calculates the sentiment polarity in the ​​emotion expressed in this data using deep neural networks and LLMs based on Bidirectional Encoder Representations from Transformers (BERT) models.

This enables marketers to use social media sentiment analysis to spot emotions in social content, messages as well as in emojis to understand customers better. In turn, your teams can anticipate customer needs and optimize plans to improve customer satisfaction and brand loyalty.

Sprout's Listening tool uses social media sentiment analysis to spot the emotion in social content, messages and emojis and understand customers better.
Challenges of AI social listening
AI social listening is a game-changer, but it’s not an easy task. Social listening tools often face common challenges that may include: